Refer to your Cloud Provider website for information about cloud hosting options and related cost information.

AWS EC2

For AWS EC2 costs visit the EC2 Pricing page and use the AWS Pricing Calculator to estimate the cost for your architecture.

This guide introduces Akamas and covers various fundamental topics such as licensing and deployment models, security topics, and maintenance & support services.

It is recommended to read this guide before moving to other guides on how to install, integrate, and use Akamas. The Glossary section of the Reference guide can help in reviewing Akamas key concepts.

Akamas is an on-premise product running on a dedicated machine within the customer environment:

• on a virtual or physical machine in your data center

• on a virtual machine managed running on a cloud, by any cloud provider (e.g. AWS EC2)

• on your own laptop

Akamas also provides a Free Trial option which can be requested here.

This page is intended as a first introduction to Akamas Maintenance & Support (M&S) Services.

Please refer to the specific contract in place with your Company.

Akamas M&S Services include:

• access to Software versions released as major and minor versions, service packs, patches, and hotfixes according to Support levels for software versions.

• assistance from Akamas Customer Support for inquiries about the Akamas product and issues encountered while using Akamas products where there is a reasonable expectation that issues are caused by Akamas products, according to Support levels for Customer Support Services

Akamas M&S Services do not include any installation and upgrade services, creation of any custom optimization packs, telemetry providers, or workflow operators, or implementation of any custom features and integrations that are not provided out-of-the-box by the Akamas products.

Akamas takes security seriously and provides enterprise-grade software where customer data is kept safe at all times. This page describes some of the most important security aspects of Akamas software and information related to processes and tools used by the Akamas company (Akamas S.p.A) to develop its software products.

Information managed by Akamas

Akamas manages the following types of information:

• System configuration and performance metrics: technical data related to optimized systems. Examples of such data include the number of CPUs available in a virtual machine or the memory usage of a Java application server;

• User accounts: accounts assigned to users to securely access the Akamas platform. For each user account, Akamas currently requires an account name and a password. Akamas does not collect any other personal identifying information;

• Service Credentials: credentials used by Akamas to automate manual tasks and to integrate with external tools. In particular, Akamas leverages the following types of interaction:

  • Integration with monitoring and orchestration tools, e.g., collecting IT performance metrics and system configuration. As a best practice, Akamas recommends using dedicated service accounts with minimal read-only privileges.

  • Integration with the target systems to apply changes to configuration parameters. As a best practice, Akamas recommends using dedicated service accounts with minimal privileges to read/write identified parameters.

GDPR Compliance

Akamas is a fully GDPR-compliant product.

Akamas is a company owned by the Moviri Group. The Moviri Group and all its companies are fully compliant with GDPR. Moviri Group Data Privacy Policy and Data Breach Incident Response Plan which apply to all the owned companies can be requested from Akamas Customer Support.

Security certifications

Akamas is an on-premises product and does not transmit any data outside the customer network. Considering the kind of data that is managed within Akamas (see section "Which information is managed by Akamas"), specific security certifications like PCI or HIPAA are not required as the platform does not manage payment or health-related information.

Data encryption

Akamas takes the need for security seriously and understands the importance of encrypting data to keep it safe at rest and in-flight.

In-Flight encryption

All the communications between Akamas UI and CLI and the back-end services are encrypted via HTTPS. The customer can configure Akamas to use customer-provided SSL certificates in all communications.

Communications between Akamas services and other integrated tools within the customer network rely on the security configuration requirements of the integrated tool (e.g.: HTTPS calls to interact with REST services).

At-Rest encryption

Akamas is an on-premises product and runs on dedicated virtual machines within the customer environment. At-rest encryption can be achieved following customer policies and best practices, for example, leveraging operating system-level techniques.

Akamas also provides an application-level encryption layer aimed at extending the scope of at-rest encryption. With this increased level of security, sensitive data managed by Akamas (e.g. passwords, tokens, or keys required to interact with external systems) are safely stored in Akamas databases using industry-standard AES 256-bit encryption.

Encryption option for Akamas on EC2

In the case of Akamas hosted on an AWS machine you may optionally create an EC2 instance with an encrypted EBS volume before installing OS and Akamas, to achieve a higher level of security.

Password management

Password Security

Passwords are securely stored using a one-way hash algorithm.

Password complexity

Akamas comes with a default password policy with the following requirements:

• has a minimum length of 8 characters.

• contains at least 1 uppercase and 1 lowercase character.

• contains at least 1 special character.

• is different from the username.

• must be different from the last password set.

Customers can modify this policy by providing a custom one that matches their internal security policies.

Password rotation

Akamas enforces no password rotation mechanism.

Credential storage

• When running on a Linux installation with KDE's KWallet enabled or GNOME's Keyring enabled, the credentials will be stored in the default wallet/keyring.

• When running on Windows, the credentials will be stored in Windows Credential Locker.

• When running on a macOS, the credential will be stored in Keychain.

• When running on a Linux headless installation, the credentials will be stored in CLEAR TEXT in a file in the current Akamas configuration folder.

Resources visibility model

Akamas provides fine granularity control over resources managed within the platform. In particular, Akamas features two kinds of resources:

• Workspace resources: entities bound to one of the isolated virtual environments (named workspaces) that can only be accessed in reading or writing mode by users to whom the administrators explicitly granted the required privileges. Such resources typically include sensitive data (e.g.: passwords, API tokens). Examples of such resources include the system to be optimized, the set of configurations, optimization studies, etc.

• Shared resources: entities that can be installed and updated by administrators and are available to all Akamas users. Such resources only contain technology-related information (e.g.: the set of performance metrics for a Java application server). Examples of such resources include Optimization Packs, which are libraries of technology components that Akamas can optimize, such as a Java application server.

Akamas Logs

Akamas logs traffic from UI and APIs. Application level logs include user access via APIs and UI and any action taken by Akamas on integrated systems.

Akamas' logs are retained on the dedicated virtual machine within the customer environment, by default, for 7 days. The retention period can be configured according to customer policies. Logs can be accessed either via UI or via log dump within the retention period. Additionally, logs have a format that can be easily integrated with external systems like log engines and SIEM to support forensic analysis.

Code scanning policy

Akamas is developed according to security best practices and the code is scanned regularly (at least daily).

The Akamas development process leverages modern continuous integration approaches and the development pipeline includes SonarQube, a leading security scanning product that includes comprehensive support for established security standards including CWE, SANS, and OWASP. Code scanning is automatically triggered in case of a new build, a release, and every night.

Vulnerability scanning and patch management policy

Akamas features modern micro-service architecture and is delivered as a set of docker containers whose images are hosted on a private Elastic Container Registry (ECR) repository on the AWS cloud. Akamas leverages the vulnerability scanning capabilities of AWS ECR to identify vulnerabilities within the product container images. AWS ECR uses the Common Vulnerabilities and Exposures (CVEs) database from the open-source Clair project.

If a vulnerability is detected, Akamas will perform a security assessment of the security risk in terms of the impact of the vulnerability, and evaluate the necessary steps (e.g.: dependency updates) required to fix the vulnerability within a timeline related to the outcome of the security assessment.

After the assessment, the vulnerability can be fixed by either recommending the upgrade to a new product version or delivering a patch or a hotfix for the current version.

Akamas is the AI-powered optimization platform designed to maximize service quality and cost efficiency without compromising on application performance. Akamas supports both production environments under live, dynamic workloads, and in test/pre-production environments against any what-if scenario and workload.

Thanks to Akamas, performance engineers, DevOps, CloudOps, FinOps and SRE teams can keep complex applications, such as Kubernetes microservices applications, optimized to avoid any unnecessary cost and any performance risks.

Akamas Optimization platform

The Akamas optimization platform leverages patented AI techniques that can autonomously identify optimal full-stack configurations driven by any custom-defined goals and constraints (SLOs), without any human intervention, any agents, and any code or byte-code changes.

Akamas optimal configurations can be applied either i) under human approval (human-in-the-loop mode) or ii) automatically, as a continuous optimization step in a CI/CD pipeline (in-the-pipe) or iii) autonomously by Akamas (autopilot).

Akamas coverage

Akamas can optimize any system with respect to any set of parameters chosen from the application, middleware, database, cloud, and any other underlying layers.

Akamas provides dozens of out-of-the-box Optimization Packs available for key technologies such as JVM, Go, Kubernetes, Docker, Oracle, MongoDB, ElasticSearch, PostgreSQL, Spark, AWS EC2 and Lambda, and more. Optimization Pack provides parameters, relationships, and metrics to accelerate the optimization process setup and support company-wide best practices. Custom Optimization Packs can be easily created without any coding.

The following figure is illustrative of Akamas coverage for both managed technologies and integrated components of the ecosystem.

Akamas integrations

Akamas can integrate with any ecosystem thanks to out-of-the-box and custom integrations with the following components:

• telemetry & monitoring tools and other sources of KPIs and cost data, such as Dynatrace, Prometheus, CloudWatch, and CSV files

• configuration management tools, repositories and interfaces to apply configurations, such as Ansible, Openshift, and Git

• value stream delivery tools to support a continuous optimization process, such as Jenkins, Dynatrace Cloud Automation, and GitLab

• load testing tools to generate simulated workloads in test/pre-production, such as LoadRunner, NeoLoad, and JMeter

Akamas has been designed around Infrastructure-as-Code (IaC) and DevOps principles. Thanks to a comprehensive set of APIs and integration mechanisms, it is possible to extend the Akamas optimization platform to manage any system and integrate with any ecosystem.

Use Cases

Akamas optimization platform supports a variety of use cases, including:

• Improve Service Quality: optimize application performance (e.g. maximize throughput, minimize response time and job execution time) and stability (lower fluctuations and peaks);

• Increase Business Agility: identify resource bottlenecks in early stages of the delivery cycle, avoid delays due to manual remediations - release higher quality services and reduce production incidents;

• Increase Service Resilience: improve service resilience under higher workloads (e.g. expected business growth) or failure scenarios identified by chaos engineering practices - improve SRE practice;

• Reduce IT Cost / Cloud Bill: reduce on-premise infrastructure cost and cloud bills due to resource over-provisioning - improve cost efficiency of Kubernetes microservices applications;

• Optimize Cloud Migration: safely migrate on-premise applications to cloud environments for optimal cost efficiency evaluate options to migrate to managed services (e.g. AWS Fargate);

• Improve Operational Efficiency: save engineering time spent on manual tuning tasks and enable Performance Engineering teams to do more in less time (and with less external consulting).

This page is intended as your entry point to the Akamas documentation.

Akamas is based on a microservices architecture where each service is deployed as a container and communicates with other services via REST APIs. Akamas can be deployed on a dedicated machine (Akamas Server) or on a Kubernetes cluster.

The following figure represents the high-level Akamas architecture.

Interact with Akamas

Users can interact with Akamas via either the Graphical User Interface (GUI), Command-Line Interface (CLI), or Application Programmatic Interface (API).

Both the GUI and CLI leverage HTTP/S APIs which pass through an API gateway (based on Kong), which also takes care of authenticating users by interacting with Akamas access management and routing requests to the different services.

The Akamas CLI can be invoked on either the Akamas Server itself or on a different machine (e.g. a laptop or another server) where the Akamas CLI has been installed.

Repositories

Akamas data is securely stored in different databases:

• time series data gathered from telemetry providers are stored in Elasticsearch;

• application logs are also stored in Elasticsearch;

• data related to systems, studies, workflows, and other user-provided data are stored in a Postgres database.

Notice: both Postgres and Elasticsearch and any other service included within Akamas are provided by Akamas as part of the Akamas installation package.

Services

Core Services

The following Spring-based microservices represent Akamas core services:

• System Service: holds information about metrics, parameters, and systems that are being optimized

• Campaign Service: holds information about optimization studies, including configurations and experiments

• Metrics Service: stores raw performance metrics (in Elasticsearch)

• Analyzer Service: automates the analysis of load tests and provides related functionalities such as smart windowing

• Telemetry Service: takes care of integrating different data sources by supporting multiple Telemetry Providers

• Optimizer Service: combines different optimization engines to generate optimized configurations using ML techniques

• Orchestrator Service: manages the execution of user-defined workflows to drive load tests

• User Service: takes care of user management activities such as user creation or password changes

• License Service: takes care of license management activities, optimization pack, and study export.

Ancillary Services

Akamas also provides advanced management features like logging, self-monitoring, licensing, user management, and more.

Before installing the Akamas Server please make sure to review all the following requirements:

• Hardware requirements

• Software requirements

• Network requirements

Different levels of support are provided for software versions of Akamas products, starting from its general availability (GA) date, and depending on the release of following software versions.

Version Numbering

Akamas adopts a three-place numbering scheme MA.MI.SP to designate released versions of its Software, where:

• MA is the Major Version

• MI is the Minor Version

• SP is the Service Pack or Patch number

Support levels

The following table describes the three levels of support for a software version.

End-of-Life (EOL)

At any time, Akamas reserves the right to "end of life" (EOL) a software product and to terminate any Maintenance & Support Services for such product, provided that Licensor has notified the Licensee at least 12 months prior to the above-mentioned termination.

The period of time occurring between the "end of life" notification and the actual termination of Maintenance & Support Services is provided as follows:

• No new enhancements will be introduced.

• No enhancements will be made to support new or updated versions of the platform on which the product runs or which it integrates.

• New hotfixes for problems of high technical impact or business exposure for customers may still be developed. Based on customer input, Akamas Support Agents will determine the degree of impact and exposure and the consequent activities.

• Reasonable efforts will be done to inform the Customer of any fixes, service packs, patches, or workarounds applicable to the reported case if any.

Software Licenses

Akamas software licensing model is subscription-based (typically on a yearly basis). For more information on Akamas' cost model and software licensing costs, please contact info@akamas.io.

Maintenance & Support Services

Akamas software licenses include Maintenance & Support Services which also include access to Customer Support Services.

Other billable services

Akamas also provides optional professional services for deployment, training, and integration activities. For more information about Akamas professional services, please contact info@akamas.io.

Akamas Customer Support Services are delivered by Akamas support engineers, also called Support Agents, who will work remotely with Customer to provide a temporary remedy for the incident and, ultimately, a permanent resolution. Akamas Support Agents automatically escalate issues to the appropriate technical group within Akamas and notify Customers of any relevant progress. Akamas provides Customers with the ability to escalate issues when appropriate.

Please notice that Customer Support services are not to be considered as alternatives to product documentation and training, or to professional and consulting services, so adequate knowledge of Akamas products is assumed when interacting with Akamas Customer Support. Thus, during the resolution of a reported issue Support Agents may redirect Customer to training or professional services (that are not part of the scope of this service).

Based on the Support levels for software versions, the following table describes the level of support of the Akamas versions after the version 3.2 GA date (2023 May, 1st).

This page will guide you through the installation of software components that are required to get the Akamas Server installed on a machine. Please read the for a detailed list of these software components for each specific OS.

While some links to official documentation and installation resources are provided here, please make sure to refer to your internal system engineering department to ensure that your company deployment processes and best practices are correctly matched.

Dependencies Setup

As a preliminary step before installing any dependency, it is strongly suggested to create a user named akamas on your machine hosting Akamas Server.

Docker

Follow the reference documentation to install docker on your system.

Docker installation guide:

Docker compose is already installed since Docker 23+. To install it on previous versions of Docker follow this installation guide:

AWS CLI v2:

Verify dependencies

As a quick check to verify that all dependencies have been correctly installed, you can run the following commands

• Docker:

• Docker compose :

• AWS CLI:

Akamas Customer Support Services provides different standard levels of support. Please verify the level of support specified in the contract in place with your Company.

Severity levels

The following table describes the different severity levels for Customer Support.

Support conditions

The contract in place with the Customer specifies the level of support provided by Akamas Agents, according at least to the following items:

• Maximum number of support seats: this is the maximum number of named users within the Customer organization who can request Akamas Customer Support.

• Language(s): these are the languages that can be used for interacting with Akamas Support Agents - the default is English.

• Channel(s): these are the different communication channels that can be used to interact with Akamas Agents - these may include one or more options among web ticketing, email, phone, and Slack channel.

• Max Initial Response Time: this refers to the time interval occurring from the time a request is opened by Customer to Customer Support and the time a Support Agent responds with a first notification (acknowledgment).

• Severity: this is the level of severity associated with a reported issue, which initially corresponds to the severity level originally indicated by the Customer. Notice that the severity level may change, for example as new information becomes available or if Support Agents and Customer agree to re-evaluate it. Please notice that the severity level may be downgraded by Support Agents if Customer is not able to provide adequate resources or responses to enable Akamas to continue with its resolution efforts.

• Initial Remedy: this refers to any operation aimed at addressing a reported issue by restoring a minimal level of operations, even if it may cause some performance degradation of the Customer service or operations. A workaround is to be considered a valid Initial Remedy.

Please notice that Support Agents may refuse to serve a service request to Customer Support either in case Customer does not have a valid Maintenance & Support subscription or in case the above-mentioned conditions or other conditions stated in the contract in place are not met. In any case, the Customer is expected to provide all the information required by Support Agent in order to serve service requests Customer Support.

Running in your data center

The following table provides the minimal hardware requirements for the virtual or physical machine used to install the Akamas server in your data center.

Running on AWS EC2

To run Akamas on an AWS Instance you need to create a new virtual machine based on one of the supported operating systems. You can refer to for step-by-step instructions on creating the instance.

As shown in the following diagram, you can create the Akamas instance in the same AWS region, Virtual Private Cloud (VPC), and private subnet as your own already existing EC2 machines and by creating/configuring a new security group that allows communication between your application instances and Akamas instance. The inbound/outbound rules of this security group must be configured as explained in the Networking Requirements section of this page.

It is recommended to use an m6a.xlarge instance with at least 70GB of disks of type GP2 or GP3 and select the latest LTS version of Ubuntu.

Supported AWS Regions

Akamas can be run in any EC2 region.

You can find the latest version supported for your preferred region .

AWS Service Limits

Before installing Akamas on an AWS Instance please make sure to meet your AWS service limits (please refer to the official AWS documentation ).

Operating System

The following table provides a list of the supported operating systems and their versions.

On RHEL systems Akamas containers might need to be run in privileged mode depending on how Docker was installed on the system.

Software packages

The following table provides a list of the required Software Packages (also referred to as Akamas dependencies) together with their versions.

The exact version of these prerequisites is listed in the following table:

Akamas user

To install and run Akamas it is recommended to create a dedicated user (usually "akamas"). The Akamas user is not required to be in the sudoers list but can be added to the docker (dockeroot) group so it can run docker and docker-compose commands.

Make sure that the Akamas user has the read, write, and execute permissions on /tmp. If your environment does not allow writing to the whole /tmp folder, please create a folder /tmp/build and assign read and write permission to the Akamas user on that folder.

Akamas is deployed as a set of containerized services running on Docker and managed via Docker Compose. The latest version of the Akamas Docker Compose file and all the images required by Docker can be downloaded from the AWS ECR repository.

Two installation modes are available:

• online installation mode, in case the Akamas Server has access to the Internet - installation behind a proxy server is also supported.

• offline installation mode, in case the Akamas Server does not have access to the Internet.

Akamas is deployed as a set of containerized services running on Docker and managed via Docker Compose. In the offline installation mode, the latest version of the Akamas Docker Compose file and all the images required by Docker cannot be downloaded from the AWS ECR repository.

Get Akamas Docker artifacts

Get in contact with Akamas Customer Services to get the latest versions of the Akamas artifacts uploaded to a location of your choice on the dedicated Akamas Server.

Akamas installation artifacts will include:

• images.tar.gz: a tarball containing Akamas main images.

• docker-compose.yml: docker-compose file for Akamas.

• akamas: the binary file of the Akamas CLI that will be used to verify the installation.

Import Docker images

A preliminary step in the offline installation mode is to import the shipped Docker images by running the following commands in the same directory where the tar files have been stored:

cd <your bundle files location>
docker image load -i images.tar.gz

Mind that this import procedure could take some time!

Configure Akamas environment variables

To configure Akamas, you should set the following environment variables:

• AKAMAS_CUSTOMER: the customer name matching the one referenced in the Akamas license.

• AKAMAS_BASE_URL: the endpoint in the Akamas APIs that will be used to interact with the CLI, typically https://<akamas server DNS address>

To avoid losing your environment variables for future upgrades, it is suggested to keep them in the .env file, stored in the same directory as the docker-compose.yml:

# Required variables
AKAMAS_CUSTOMER=<your name or your organization name>
AKAMAS_BASE_URL=https://<akamas server DNS address>

# Optional variables
## Database password. Use DEFAULT_DATABASE_PASSWORD to set a custom password for all databases
DEFAULT_DATABASE_PASSWORD=
## A custom password per each service can be set using the variables below, otherwise, the default is used. For example, for the kong database, the password is `akamas_kong`.
KONG_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_kong}
AIRFLOW_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_airflow}
KEYCLOAK_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_keycloak}
ANALYZER_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_analyzer}
CAMPAIGN_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_campaign}
LICENSE_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_license}
OPTIMIZER_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_optimizer}
ORCHESTRATOR_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_orchestrator}
SYSTEM_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_system}
TELEMETRY_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_telemetry}

Run installation

To start Akamas you can now simply navigate into the akamas folder and run a docker-compose command:

cd <your docker-compose file location>
docker compose up -d

You may get the following error:

Error saving credentials: error storing credentials - err: exit status 1, out: Cannot autolaunch D-Bus without X11 $DISPLAY

This is a documented docker bug (see this link) that can be solved by installing the "pass" package:

• Ubuntu

sudo apt-get install -y pass

• RHEL

yum install pass

This section describes how to setup an Akamas Server behind a proxy server and to allow Docker to connect to the Akamas repository on AWS ECR.

Configure Docker daemon

First, create the /etc/systemd/system/docker.service.d directory if it does not already exists. Then create or update the /etc/systemd/system/docker.service.d/http-proxy.conf file with the variables listed below, taking care of replacing <PROXY> with the address and port (and credentials if needed) of your target proxy server:

[Service]
Environment="HTTP_PROXY=<PROXY>"
Environment="HTTPS_PROXY=<PROXY>"

Once configured, flush the changes and restart Docker with the following commands:

sudo systemctl daemon-reload
sudo systemctl restart docker

For more details, refer to the official documentation page: Control Docker with systemd.

Configure the Akamas containers

To allow the Akamas services to connect to addresses outside your intranet, the Docker instance needs to be configured to forward the proxy configuration to the Akamas containers.

Update the ~/.docker/config.json file adding the following field to the JSON, taking care to replace <PROXY> with the address (and credentials if needed) of your target proxy server:

{
  # ...
  "proxies": {
    "default": {
      "httpProxy": "<PROXY>",
      "httpsProxy": "<PROXY>",
      "ftpProxy": "<PROXY>",
      "noProxy": "localhost,127.0.0.1,/var/run/docker.sock,database,optimizer,campaign,analyzer,telemetry,log,elasticsearch,metrics,system,license,store,orchestrator,airflow-db,airflow-webserver,kong-database,kong,user-service,keycloak,logstash,kibana,akamas-ui,grafana,prometheus,node-exporter,cadvisor,konga,benchmark"
    }
  }
}

For more details, refer to the official documentation page: Configure Docker to use a proxy server.

Run Akamas

Set the following variables to configure your working environment, taking care to replace <PROXY> with the address (and credentials if needed) of your target proxy server:

export HTTP_PROXY='<PROXY>'
export HTTPS_PROXY='<PROXY>'

Once configured, you can log into the ECR repository through the AWS CLI and start the Akamas services manually.

Akamas is deployed as a set of containerized services running on Docker and managed via Docker Compose. In the online installation mode, the latest version of the Akamas Docker Compose file and all the images required by Docker can be downloaded from the AWS ECR repository.

In case the Akamas Server is behind a proxy server please also read how to setup Akamas behind a Proxy.

Get Akamas Docker artifacts

It is suggested first to create a directory akamas in the home directory of your user, and then run the following command to get the latest compose file:

cd ~
mkdir akamas
cd akamas
curl -O https://s3.us-east-2.amazonaws.com/akamas/compose/3.3.1/docker-compose.yml

Configure Akamas environment variables

To configure Akamas, you should set the following environment variables:

• AKAMAS_CUSTOMER: the customer name matching the one referenced in the Akamas license.

• AWS_ACCESS_KEY_ID: the access key for pulling the Akamas images

• AWS_SECRET_ACCESS_KEY: the secret access key for pulling the Akamas images

• AWS_DEFAULT_REGION: Unless specified by the support team keep the value to us-east-2

• AKAMAS_BASE_URL: the endpoint in the Akamas APIs that will be used to interact with the CLI, typically https://<akamas server DNS address>

To avoid losing your environment variables for future upgrades, it is suggested to keep them in the .env file, stored in the same directory as the docker-compose.yml:

# Required variables
AKAMAS_CUSTOMER=<your name or your organization name>
AWS_ACCESS_KEY_ID=<your access key id>
AWS_SECRET_ACCESS_KEY=<your secret access key>
AKAMAS_BASE_URL=https://<akamas server DNS address>
AWS_DEFAULT_REGION=us-east-2

# Optional variables
# Database passwords
DEFAULT_DATABASE_PASSWORD=
KEYCLOAK_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_keycloak}
ANALYZER_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_analyzer}
CAMPAIGN_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_campaign}
LICENSE_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_license}
OPTIMIZER_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_optimizer}
ORCHESTRATOR_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_orchestrator}
SYSTEM_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_system}
TELEMETRY_DATABASE_PASSWORD=${DEFAULT_DATABASE_PASSWORD:-akamas_telemetry}

Start Akamas

To log into AWS ECR and pull the most recent Akamas container images you also need to set the AWS authentication variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION) with the values provided by Akamas Customer Support Services. You can leverage the .env file previously created with the following command:

source ./.env
aws ecr get-login-password --region us-east-2 | docker login -u AWS --password-stdin https://485790562880.dkr.ecr.us-east-2.amazonaws.com

You can start installing Akamas server by running the following AWS CLI commands:

docker compose up -d

Preliminary steps

Please make sure to read the Getting Started section before installing Akamas.

Before installing Akamas, please follow these steps:

1. Review hardware, software, and network prerequisites

2. Install all Akamas dependencies

Installation steps

Please follow these steps to install the Akamas Server:

1. Install the Akamas Server

2. Install the Akamas CLI

3. Verify the Akamas Server

4. Install an Akamas license

Please also read the section on how to troubleshoot the installation and how to manage the Akamas Server. Finally, read the relevant sections of Integrating Akamas to integrate Akamas into your specific ecosystem.

Internet access

Internet access is required for Akamas online installation and updated procedures and allows retrieving the most updated Akamas container images from the Akamas private Amazon Elastic Container Registry (ECR).

If internet access is not available for policies or security reasons, Akamas installation and updates can be executed offline.

Internet access from the Akamas server is not mandatory but it’s strongly recommended.

Ports

The following table provides a list of the ports on the Akamas server that have to be reachable by Akamas administrators and users to properly operate the system.

In the specific case of AWS instance and customer instances sharing the same VPC/Subnet inside AWS, you should:

• open all of the ports listed in the table above for all inbound URLs (0.0.0.0/32) on your AWS security group

• open outbound rules to all traffic and then attach this AWS security group (which must reside inside a private subnet) to the Akamas machine and all customer application AWS machines

Issues when installing Docker

Centos 7 and RHEL 7

Notice: this distro features a known issue since Docker default execution group is named dockerroot instead of docker . To make docker work edit (or create) /etc/docker/daemon.json to include the following fragment:

{
  "group": "dockerroot"
}

After editing or creating the file, please restart Docker and then check the group permission of the Docker socket (/var/run/docker.sock), which should show dockerroot as a group:

srw-rw----. 1 root dockerroot 0 Jul  4 09:57 /var/run/docker.sock

Then, add the newly created akamas user to the dockerroot group so that it can run docker containers:

sudo usermod -aG dockerroot <user_name>

and check the akamas user has been correctly added to dockerroot group by running:

lid -g dockerroot

Issues when running AWS CLI

In case of issues in logging in through AWS CLI, when executing the following command:

aws ecr get-login-password --region us-east-2

Please check that:

• Environment variables AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION are correctly set

• AWS CLI version is 2.0+

We recommend using the official AWS CLI installation guide for a smoother experience.

Issue when starting Akamas services

Akamas failed to start some services

Please notice that the very first time Akamas is started, up to 30 minutes might be required to initialize the environment.

In case the issue persists you can run the following command to identify which service is not able to start up correctly

akamas status -d

License service unable to access docker socket

In some systems, the Docker socket, usually located in /var/run/docker.sock can not be accessed within a container. This causes Akamas to signal this behavior by reporting the Access Denied error in the license service logs.

To overcome this limitation edit the docker-compose.yaml file adding the line privileged: true to the following services:

• License

• Optimizer

• Telemetry

• Airflow

The following is a sample configuration where this change is applied to the license service:

license:
  image: 485790562880.dkr.ecr.us-east-2.amazonaws.com/akamas/license_service:2.3.0
  container_name: license
  privileged: true

Finally, you can issue the following command to apply these changes

docker compose up -d

Missing Akamas Customer variable

When installing Akamas it’s mandatory to provide the AKAMAS_CUSTOMER variable as illustrated in the installation guide. This variable must match the one provided by Akamas representatives when issuing a license. If the variable is not properly exported license installation will fail with an error message indicating that the name of the customer installation does not match the one provided in the license.

You can easily inspect which value of this variable has been used when starting Akamas by running the following command on the Akamas server:

docker inspect license | grep AKAMAS_CUSTOMER

If you find out that the value is not the one you expect, you can update the .env file and then start again the license service by running:

docker compose up -d license

Once Akamas is up and running you can re-install your license.

Other issues

For any other issues please contact Akamas Customer Support Services.

Akamas is deployed on your Kubernetes cluster through a , and all the required images can be downloaded from the AWS ECR repository.

Two installation modes are available:

• , in case the Kubernetes cluster can access the Internet.

• , in case the Kubernetes cluster does not have access to the Internet or you need to use a private image registry.

This page describes the requirements that should be fulfilled by the user when installing or managing an Akamas installation on Kubernetes. The software below is usually installed on the user's workstation or laptop.

Kubectl

Kubectl must be installed and configured to interact with the desired cluster. Refer to the to set up the client.

To interact with the Kubernetes APIs, you will need , preferably with a version matching the cluster. To check both the client and cluster versions, run the following:

Helm

Installing Akamas requires or higher. To check the version, run the following:

Privileged access

Akamas uses Elasticsearch to store logs and time series. When running Akamas on Kubernetes, Elasticsearch is installed automatically using the official Elasticsearch helm chart. This chart required running an init container with privileged access to set up a configuration on the Elasticsearch pod host. If running such a container is not permitted in your environment, you can add the following snippet to the akamas.yaml file when installing Akamas to disable this feature.

Preliminary steps

Before installing Akamas, please follow these steps:

Installation steps

Please follow these steps to install the Akamas application:

Please also read the section on how to . Finally, read the relevant sections of to integrate Akamas into your specific ecosystem.

Before installing the Akamas please make sure to review all the following requirements:

Akamas APIs and UI use plain HTTP when they are first installed. To enable the use of HTTPS you will need to:

1. Ask your security team to provide you with a valid certificate for your server. The certificate usually consists of two files with ".key" and ".pem" extensions. You will need to provide the Akamas server DNS name.

2. Create a folder named "certs" in the same directory as Akamas' docker-compose file;

3. Copy the ".key" and ".pem" files in the created "certs" folder and rename them to "akamas.key" and "akamas.pem" respectively. Make sure that the files belong to the same user and group you use to run Akamas.

4. Restart two Akamas services by running the following commands:

After the containers reboot is complete you will be able to access the UI over HTTPS from your browser:

Setup CLI to use HTTPS

Now that your Akamas server is configured to use HTTPS you can update the Akamas CLI configuration in order to use the secure protocol.

If you have not yet installed the Akamas CLI follow the in order to install it. If you already have the CLI available, you can run the following command:

You will be prompted to enter some input, please value it as follows:

You can test the connection by running:

It should return ‘OK’, meaning that Akamas has been properly configured to work over HTTPS.

Kubernetes version

Running Akamas requires a cluster running Kubernetes version 1.24 or higher.

Resources requirements

Akamas can be deployed in three different sizes depending on the number of concurrent optimization studies that will be executed. If you are unsure about which size is appropriate for your environment we suggest you start with the small one and upgrade to bigger ones as you expand the optimization activity to more applications.

The tables below report the required resources both for requests and limits that should be available in the cluster to use Akamas.

Small

The small tier is suited for environments that need to support up to 10 concurrent optimization studies

Storage requirements

The cluster must provide the definition of a Storage Class so that the application installation can leverage Persistent Volume Claims to dynamically provision the volumes required to persist data.

For more information on this topic refer to .

Permissions

To install and run Akamas cluster level permissions are not required. This is the minimal set of namespaced rules.

Networking

Before starting the installation, make sure the requirements are met.

Create the configuration file

Akamas on Kubernetes is provided as a set of templates packaged in a chart archive managed by Helm.

To proceed with the installation, you need to create a Helm Values file, called akamas.yaml in this guide, containing the mandatory configuration values required to customize your application. The following template contains the minimal set required to install Akamas:

# AWS credentials to fetch ECR images (required)
awsAccessKeyId: <AWS_ACCESS_KEY_ID>
awsSecretAccessKey: <AWS_SECRET_ACCESS_KEY>

# Akamas customer name. Must match the value in the license (required)
akamasCustomer: <CUSTOMER_NAME>

# Akamas administrator password. If not set a random password will be generated
akamasAdminPassword: <ADMIN_PASSWORD>

# The URL that will be used to access Akamas, for example 'http://akamas.kube.example.com' (required)
akamasBaseUrl: <INSTANCE_HOSTNAME>

You can also download the template file running the following snippet:

curl -so akamas.yaml  http://helm.akamas.io/templates/1.3.0/akamas.yaml.template

Replace in the file the following placeholders:

• AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY: the AWS credentials for pulling the Akamas images

• CUSTOMER_NAME: customer name provided with the Akamas license

• ADMIN_PASSWORD: initial administrator password

• INSTANCE_HOSTNAME: the URL that will be used to expose the Akamas installation, for example https://akamas.k8s.example.com when using an Ingress, or http://localhost:9000 when using port-forwarding. Refer to Accessing Akamas for the list of the supported access methods and a reference for any additional configuration required.

Start the installation

With the configuration file you just created (and the new variables you added to override the defaults), you can start the installation with the following command:

helm upgrade --install \
  --create-namespace --namespace akamas \
  --repo http://helm.akamas.io/charts \
  --version '1.3.0' \
  -f akamas.yaml \
  akamas akamas

This command will create the Akamas resources within the specified namespace. You can define a different namespace by changing the argument --namespace <your-namespace>

An example output of a successful installation is the following:

Release "akamas" does not exist. Installing it now.
NAME: akamas
LAST DEPLOYED: Thu Sep 21 10:39:01 2023
NAMESPACE: akamas
STATUS: deployed
REVISION: 1
NOTES:
Akamas has been installed

NOTES:
Akamas has been installed

To get the initial password use the following command:

kubectl get secret akamas-admin-credentials -o go-template='{{ .data.password | base64decode }}'

Check the installation

To monitor the application startup, run the command kubectl get pods. After a few minutes, the expected output should be similar to the following:

NAME                           READY   STATUS    RESTARTS   AGE
airflow-6ffbbf46d8-dqf8m       3/3     Running   0          5m
analyzer-67cf968b48-jhxvd      1/1     Running   0          5m
campaign-666c5db96-xvl2z       1/1     Running   0          5m
database-0                     1/1     Running   0          5m
elasticsearch-master-0         1/1     Running   0          5m
keycloak-66f748d54-7l6wb       1/1     Running   0          5m
kibana-6d86b8cbf5-6nz9v        1/1     Running   0          5m
kong-7d6fdd97cf-c2xc9          1/1     Running   0          5m
license-54ff5cc5d8-tr64l       1/1     Running   0          5m
log-5974b5c86b-4q7lj           1/1     Running   0          5m
logstash-8697dd69f8-9bkts      1/1     Running   0          5m
metrics-577fb6bf8d-j7cl2       1/1     Running   0          5m
optimizer-5b7576c6bb-96w8n     1/1     Running   0          5m
orchestrator-95c57fd45-lh4m6   1/1     Running   0          5m
store-5489dd65f4-lsk62         1/1     Running   0          5m
system-5877d4c89b-h8s6v        1/1     Running   0          5m
telemetry-8cf448bf4-x68tr      1/1     Running   0          5m
ui-7f7f4c4f44-55lv5            1/1     Running   0          5m
users-966f8f78-wv4zj           1/1     Running   0          5m

At this point, you should be able to access the Akamas UI using the endpoint specified in the akamasBaseUrl, and interact through the Akamas CLI with the path /akapi.

If you haven't already, you can update your configuration file to use a different type of service to expose Akamas' endpoints. To do so, pick from the Accessing Akamas the configuration snippet for the service type of your choice, add it to the akamas.yaml file, update the akamasBaseUrl value, and re-run the installation command to update your Helm release.

The Akamas CLI allows users to invoke commands against the Akamas dedicated machine (Akamas Server). The Akamas CLI can also be installed on a different system than the Akamas Server.

Prerequisites

Linux and Windows operating systems are supported for installing Akamas CLI.

Installation steps

The Akamas CLI can be installed and configured in two simple steps:

1. Setup the CLI

2. Initialize the CLI

Refer to the section Change CLI config to modify the CLI ports the Akamas Server is listening to. Section Use a proxy server provides instructions on how to interact with Akamas via a proxy server.

Before starting the installation, make sure the requirements are met.

Configure the registry

If your cluster is in an air-gapped network or is unable to reach the Akamas image repository, you need to copy the required images to your private registry.

The procedure described here leverages your local environment to upload the images. Thus, to interact between the Akamas and private registry, it requires Docker to be installed and configured.

Transfer the Docker images

The offline installation requires you to pull the images and migrate them to your private registry. In the following command replace the chart version to download the related list of images:

curl -sO  http://helm.akamas.io/images/1.3.0/image-list

Once the import is complete, you must re-tag and upload the images. Run the following snippet, replacing <REGISTRY_URL> with the actual URL of the private registry:

NEW_REGISTRY="<REGISTRY_URL>"
while read IMAGE; do
    REGISTRY=$(echo "$IMAGE" | cut -d '/' -f 1)
    REPOSITORY=$(echo "$IMAGE" | cut -d ':' -f 1 | cut -d "/" -f2-)
    TAG=$(echo "$IMAGE" | cut -d ':' -f 2)

    NEW_IMAGE="$NEW_REGISTRY/$REPOSITORY:$TAG"
    echo "Migrating $IMAGE to $NEW_IMAGE"

    docker pull "$IMAGE"
    docker tag "$IMAGE" "$NEW_IMAGE"
    docker push "$NEW_IMAGE"
done <image-list

This process could last several minutes, once the upload is complete, you can proceed with the next steps.

Create the configuration file

Akamas on Kubernetes is provided as a set of templates packaged in a chart archive managed by Helm.

To proceed with the installation, you must create a Helm Values file, called akamas.yaml in this guide, containing the mandatory configuration values required to customize your application. The following template contains the minimal set required to install Akamas:

# Akamas customer name. Must match the value in the license (required)
akamasCustomer: <CUSTOMER_NAME>

# Akamas administrator password. If not set a random password will be generated
akamasAdminPassword: <ADMIN_PASSWORD>

# The URL that will be used to access Akamas, for example 'http://akamas.kube.example.com' (required)
akamasBaseUrl: <INSTANCE_HOSTNAME>

# The URL of your private registry
global:
  imageRegistry: <REGISTRY_URL>

postgresql:
  image:
    registry: <REGISTRY_URL>
elasticsearch:
  image: <REGISTRY_URL>/akamas/elastic/elasticsearch

Replace in the file the following placeholders:

• CUSTOMER_NAME: customer name provided with the Akamas license

• ADMIN_PASSWORD: initial administrator password

• INSTANCE_HOSTNAME: the URL that will be used to expose the Akamas installation, for example https://akamas.k8s.example.com when using an Ingress, or http//:localhost:9000 when using port-forwarding. Refer to Accessing Akamas for the list of the supported access methods and a reference for any additional configuration required.

• REGISTRY_URL: the URL for the private registry used in the transfer process above

Configure the authentication

This section describes how to configure the authentication to your private registry. If your registry does not require any authentication, skip directly to the installation section.

To authenticate to your private registry, you must manually create the Secret required to pull the images. If the registry uses basic authentication, you can create the credentials in the namespace by running the following command:

kubectl create secret docker-registry registry-token \
  --namespace akamas \
  --docker-server=<REGISTRY_URL> \
  --docker-username=<USER> \
  --docker-password=<PASSWORD>

Otherwise, you can leverage any credential already configured on your machine by running the following command:

kubectl create secret docker-registry registry-token \
  --namespace akamas \
  --from-file=.dockerconfigjson=<PATH/TO/.docker/config.json>

Start the installation

From a machine that can reach the endpoint, run the following command to download the chart:

helm pull --repo http://helm.akamas.io/charts --version '1.3.0' akamas

The command downloads the latest version chart version as an archive named akamas-<version>.tgz. The file can be transferred to the machine where the installation will be run. Replace akamas/akamas with the download package in the following commands.

If you wish to see and override the values that Helm will use to install Akamas, you may execute the following command.

helm show values akamas-<version>.tgz

Now, with the configuration file you just created (and the new variables you added to override the defaults), you can start the installation with the following command:

helm upgrade --install \
  --create-namespace --namespace akamas \
  -f akamas.yaml \
  akamas akamas-<version>.tgz

This command will create the Akamas resources within the specified namespace. You can define a different namespace by changing the argument --namespace <your-namespace>

An example output of a successful installation is the following:

Release "akamas" does not exist. Installing it now.
NAME: akamas
LAST DEPLOYED: Thu Sep 21 10:39:01 2023
NAMESPACE: akamas
STATUS: deployed
REVISION: 1
NOTES:
Akamas has been installed

NOTES:
Akamas has been installed

To get the initial password use the following command:

kubectl get secret akamas-admin-credentials -o go-template='{{ .data.password | base64decode }}'

Check the installation

To monitor the application startup, run the command kubectl get pods. After a few minutes, the expected output should be similar to the following:

NAME                           READY   STATUS    RESTARTS   AGE
airflow-6ffbbf46d8-dqf8m       3/3     Running   0          5m
analyzer-67cf968b48-jhxvd      1/1     Running   0          5m
campaign-666c5db96-xvl2z       1/1     Running   0          5m
database-0                     1/1     Running   0          5m
elasticsearch-master-0         1/1     Running   0          5m
keycloak-66f748d54-7l6wb       1/1     Running   0          5m
kibana-6d86b8cbf5-6nz9v        1/1     Running   0          5m
kong-7d6fdd97cf-c2xc9          1/1     Running   0          5m
license-54ff5cc5d8-tr64l       1/1     Running   0          5m
log-5974b5c86b-4q7lj           1/1     Running   0          5m
logstash-8697dd69f8-9bkts      1/1     Running   0          5m
metrics-577fb6bf8d-j7cl2       1/1     Running   0          5m
optimizer-5b7576c6bb-96w8n     1/1     Running   0          5m
orchestrator-95c57fd45-lh4m6   1/1     Running   0          5m
store-5489dd65f4-lsk62         1/1     Running   0          5m
system-5877d4c89b-h8s6v        1/1     Running   0          5m
telemetry-8cf448bf4-x68tr      1/1     Running   0          5m
ui-7f7f4c4f44-55lv5            1/1     Running   0          5m
users-966f8f78-wv4zj           1/1     Running   0          5m

At this point, you should be able to access the Akamas UI using the endpoint specified in the akamasBaseUrl, and interact through the Akamas CLI with the path /akapi.

If you haven't already, you can update your configuration file to use a different type of service to expose Akamas' endpoints. To do so, pick from the Accessing Akamas the configuration snippet for the service type of your choice, add it to the akamas.yaml file, update the akamasBaseUrl value, and re-run the installation command to update your Helm release.

The CLI configuration contains the information required to communicate with the akamas server. It can be easily created and updated with a configuration wizard. This page describes the main options of the Akamas CLI and how to modify them. If your Akamas instance is installed with Kubernetes, ensure the UI service is configured correctly.

API Address

The CLI, as well as the UI, interacts with the akamas server via APIs. The apiAddress configuration contains the information required to communicate with the server.

Docker

The Akamas Server provides different listeners to interact with APIs:

• an HTTP listener on port 80 under the path /akapi

• an HTTP listener on port 8000

• an HTTPS listener on port 443 under the path /akapi

• an HTTPS listener on port 8443

Depending on your networking setup you can either use the listeners on ports 80 and 443 which are also used for the UI or directly interact with the API gateway on ports 8000 and 8443. If you are unsure about your network setup we suggest you start with the HTTPS listener on port 443.

For improved security, it is recommended to configure CLI communications with the Akamas Server over HTTPS. Notice that you need to have a valid certificate installed on your Akamas server (at least a self-signed one) to enable HTTPS communication between CLI and the Akamas Server.

Changing CLI protocol

The CLI can be configured either directly via the CLI itself or via the YAML configuration file akamasconf.

Using the CLI

Issue the following command to change the configuration of the Akamas CLI:

akamas init config

and then follow the wizard to provide the required CLI configuration:

• enable HTTPS communications:

Api address [http://localhost:8000]: https://<akamas server dns name>:443/akapi
Workspace [default]: Workspace1
Login method (local, oauth2) [local]: local
Verify SSL: [True]: True
Is external certificate CA required? [y/N]: N

• enable HTTP communications:

Api address [http://localhost:8000]: http://<akamas server DNS name>:80
Workspace [default]: Workspace1
Login method (local, oauth2) [local]: local

Please notice that by default Akamas CLI expects a valid SSL certificate. If you are using a self-signed certificate or a not valid one you can set the Verify SSL variable to false. This will mimic the behavior of accepting an invalid HTTPS certificate on your favorite browser.

Using the akamasconf file

Create a file and name it akamasconf to be located at the following locations:

• Linux: ~/.akamas/akamasconf

• Windows: C:\Users\<username>\.akamas (where C: is the drive where the OS is installed)

The file location can be customized by setting an $AKAMASCONF environment variable.

Here is an example akamasconf file provided as a sample:

apiAddress: http[s]://<akamas server dns name>:80[443]/akapi
verifySsl: [true|false]
workspace: default

The Akamas CLI supports interacting with the API server through an HTTP/HTTPS proxy server.

To enable access via an HTTP proxy, set the environment variable HTTP_PROXY. From the following snippet, replace proxy_ip and proxy_port with the desired values.

export HTTP_PROXY="http://<proxy_ip>:<proxy_port>"

Then, run the akamas command to verify access.

akamas status debug

Access through an HTTPS proxy can be set by using the environment variable HTTPS_PROXY instead of HTTP_PROXY.

Linux

To get Akamas CLI installed on Linux, run the following commands:

curl -o akamas_cli https://s3.us-east-2.amazonaws.com/akamas/cli/$(curl -s https://s3.us-east-2.amazonaws.com/akamas/cli/stable.txt)/linux_64/akamas
sudo mv akamas_cli /usr/local/bin/akamas
chmod 755 /usr/local/bin/akamas

You can now run the Akamas CLI following by running the akamas command.

In some installations, the /usr/local/bin folder is not present in the PATH environment variable. This prevents you from using akamas without specifying the complete file location. To fix this issue you can add an entry to the PATH system environment variable or move the executable to another folder in your PATH.

Auto-completion

To enable auto-completion on Linux systems with a bash shell (requires bash 4.4+), run the following commands:

curl -O https://s3.us-east-2.amazonaws.com/akamas/cli/$(curl -s https://s3.us-east-2.amazonaws.com/akamas/cli/stable.txt)/linux_64/akamas_autocomplete.sh
mkdir -p ~/.akamas
mv akamas_autocomplete.sh ~/.akamas
echo '. ~/.akamas/akamas_autocomplete.sh' >> ~/.bashrc
source ~/.bashrc

Windows

To install the Akamas CLI on Windows run the following command from Powershell:

Invoke-WebRequest "https://s3.us-east-2.amazonaws.com/akamas/cli/$($(Invoke-WebRequest https://s3.us-east-2.amazonaws.com/akamas/cli/stable.txt | Select-Object -Expand Content) -replace '\n', '')/win_64/akamas.exe" -OutFile akamas.exe

You can now run the Akamas CLI by running .\akamas in the same folder.

To invoke the akamas CLI from any folder, create a akamas folder (such as C:\Program Files\akamas), and move there the akamas.exe file. Then, add an entry to the PATH system environment variable with the value C:\Program Files\akamas. Now, you can invoke the CLI from any folder, by simply running the akamas command.

The Akamas CLI can be accessed by simply running the akamascommand.

Verify the CLI

You can verify that the CLI was installed correctly by running this command:

akamas version

which should show an output similar to this one

Akamas CLI: 2.9.0
Akamas platform: 3.3.1

At any time, you can see available commands and options with:

akamas --help

For the full list of Akamas commands please refer to the section CLI reference.

To interact with your Akamas instance, you need the UI and API Gateway to be accessible from outside the cluster.

Kubernetes offers different options to expose a service outside of the cluster. The following is a list of the supported ones, with examples of how to configure them to work in your chart release:

Port Forwarding

By default, Akams uses Cluster IPs for its services, allowing communication only inside the cluster. Still, you can leverage Kubectl's port-forward to create a private connection and expose any internal service on your local machine.

This solution is suggested to perform quick tests without exposing the application or in scenarios where cluster access to the public is not allowed.

Set akamasBaseUrl to http://localhost:9000 in your Helm Values file, and install or update your Akamas deployment using the Helm command. Once the rollout is complete, open a tunnel to the UI with the following command:

As long as the port-forwarding is running, you will be able to interact with the UI through the tunnel; you can also interact through the Akamas CLI by configuring the URL http://localhost:9000/akapi.

Refer to the official for more details about port-forwarding.

Ingress

An Ingress is a Kubernetes object that provides service access, load balancing, and SSL termination to Kubernetes services.

To expose the Akamas UI through an Ingress, configure the Helm Values file by configuring akamasBaseUrl with the host of the Ingress (e.g.: https://akamas.kube.example.com), and by adding the snippet below:

Here is a description of the fields:

• enabled: set to true to enable the Ingress

Run the following command to verify the correct startup and initialization of Akamas:

When all services have been started this command will return an "OK" message. Please notice that it might take a few minutes for Akamas to start all services.

To check that also UI is properly working please access the following URL:

You will see the Akamas login form:

This page provides a walkthrough to configure Azure Active Directory as an external identity provider for Akamas users.

Configure the App registration

To integrate Akamas with your Active Directory, you first need a dedicated App registration in your Azure Organization. If you want to use an existing registration, skip to ; to create a new one, refer to the following sub-section.

Create a new App registration

To create a new registration, navigate to in your Azure portal and select "New registration" and specify the following:

• a name for the application

• the account type that best suits your use case

Complete the creation by clicking on "Register".

Get the client configuration

On the "Overview" page of the application, note the following values:

• Application (client) ID

• OpenID Connect metadata document (found in the "Endpoints" side panel)

Furthermore, in the "Certificates & secrets" section, create a new Client secret and note its value.

With these values, we can now complete the provider configuration in the Keycloak console.

Create the Identity provider

Here, specify an alias for the client ("microsoft" in our case) and optionally the display name used in the login page ("Microsoft").

In the "OpenID Connect settings" section, configure the following fields using the values from the app registration in the Azure portal:

• "Discovery endpoint": populate with the URL of the "OpenID Connect metadata document". This box should become green upon successful validation.

• "Client ID": populate with the "Application (client) ID" from the app's overview page

• "Client Secret": populate with the value of the generated secret

Complete the configuration by clicking "Add". You will land on the detail page of the new provider: here, copy the value of the redirect URI.

Complete the app registration

Back to the app registration in the Azure portal, navigate to the "Authentication" section. Add the "Web" platform (if not already present).

Finally, add to the list of redirect URIs the one from the previous step.

You have now configured Akamas to delegate to Azure your users' login.

Configure the default Akamas roles

The final setup step is to instruct Akamas to associate the default roles with the users automatically. This way, users will be added to the default workspace with read and write permissions the first time they log in.

On the Keycloak console, on the provider's details page, navigate to "Mappers":

Now, add the following configurations.

User role

• Name: User role

• Mapper type: Hardcoded role

• Role: USER

Default Workspace Read

• Name: Default Workspace Read

• Mapper type: Hardcoded role

• Role: WS_ac8481d3-d031-4b6a-8ae9-c7b366f027e8_R

Default Workspace Write

• Name: Default Workspace Write

• Mapper type: Hardcoded role

• Role: WS_ac8481d3-d031-4b6a-8ae9-c7b366f027e8_W

Test the integration

Visit the installation's login page to check that the new authentication method is displayed and works correctly.

You may find helpful some of the commands listed in the sections below.

Read database passwords

By default, access to each service database is assigned to a user with randomly generated passwords. For example, to read the campaign service database password, execute the following command:

The username for the campaign service can be found in the configuration file under each service section. To read the username for the campaign service set during the installation, launch the following command:

You can connect to the campaign_service database with the user and password above.

If you want to show all the passwords, execute this command:

Running Akamas' studies requires a valid license.

To install a license get in touch with Akamas Customer Service to receive:

• the Akamas license file

• your "customer name" to configure in the variable AKAMAS_CUSTOMER for Docker installations or akamasCustomer for Kubernetes installations

• the URL to configure in the AKAMAS_BASE_URL variable for Docker installations

• login credentials

Once you have this information, you can issue the following commands:

Akamas might collect anonymized usage information on running optimizations. Collection and tracking are disabled by default and can be manually enabled.

Docker installation

External tracking is managed through the following environment variables:

• AKAMAS_TRACKER_URL: the target URL for all tracking info.

• AKAMAS_TRACKING_OPT_OUT: when set to 1, disables anonymous data collection.

Tracking for a running instance can be enabled by editing the AKAMAS_TRACKING_OPT_OUT variable in the docker-compose.yaml file.

To enable tracking set the variable to the following value:

Then issue the command:

Kubernetes installation

External tracking is managed through the field trackingOptOut in the Values file. To enable tracking set trackingOptOut to 0 as in the following example and upgrade the installation:

Akamas offers, as an additional container, a toolbox that contains the Akamas CLI executable, along with some other useful command-line tools such as kubectl, Helm, vim, docker cli, jq, yq, git, gzip, zip, OpenSSH, ping, cURL, and wget. It can be executed along akamas services, in the same network, for docker-compose installation, or in the akamas namespace for Kubernetes installations.

This toolbox aims to:

• allowing users to interact with Akamas without the need to install Akamas CLI on their systems

• providing the with an environment where to run scripts and persist artifacts when no other options (e.g. a dedicated host) are available

Docker compose installation

By setting the following options in the .env file, you can configure your toolbox by enabling SSH password authentication (only key-based authentication will be available otherwise) and by setting a login password:

To start the toolbox container just issue the following command:

If you want to keep the toolbox running also after a complete restart you can also add the following line to your .env file: COMPOSE_PROFILES=toolbox

Accessing the toolbox on Docker

To access the toolbox on docker you can issue the following command:

You will be provided with a shell inside the toolbox where you can interact with Akamas. Please read the work folder section below for more information on how to persist scripts and data on the toolbox upon restart and upgrades.

Kubernetes installation

Follow the usual guide for installing Akamas on Kubernetes, adding the following variables to your akamas.yaml file:

Accessing the toolbox on Kubernetes

When it's deployed to Kubernetes, you may access this toolbox in two ways:

• via kubectl

• via SSH command

Kubectl access

Accessing is as simple as:

SSH access

For this type of access, you need to retrieve the SSH login password (if enabled) or key. To fetch them, run the following commands:

With this info, you can leverage the toolbox to run commands in your workflows, like in the following example:

You can also access the toolbox by port-forwarding from your local machine (on port 2222 in our example). Run the following kubectl command:

On another terminal, run:

and answer yes to the question, then insert the akamas password to successfully SSH access the toolbox (see example below):

Work directory

If you need to store Akamas artifacts, scripts, or any other file that requires persistence, you can use the /work directory, which persists across restarts. This is the default working directory at login time.

To configure an external identity provider, access the Keycloak administration console exposed on the /auth page of your installation; for example, https://app.akamas.io/auth.

Now log into the Administration Console using the admin user. The password for such a user can be retrieved in different ways, depending on the installation method:

• Kubernetes. A custom password can be specified during the installation by providing a value keycloak.adminPassword in the helm chart. If this value was left unspecified, you can retrieve the auto-generated password with the following command:

Note that you might need to provide the namespace in which Akamas have been installed using the flag -n namespace

• Docker.

  A custom password can be specified during the installation by providing a value for the variable KEYCLOAK_ADMIN_PASSWORD in the environment or in the docker-compose file. if during the installation you didn't specify the value, you can retrieve the auto-generated password with the following command:

Once logged in, select the akamas realm from the dropdown menu and navigate to the Identity providers section.

From here on, the steps required to proceed with the configuration vary depending on the provider you are integrating with. Select yours from the guides below:

Akamas audit logs

Akamas stores all its logs into an internal Elasticsearch instance: some of these logs are reported to the user in the GUI in order to ease the monitoring of workflow executions, while other logs are only accessible via CLI and are mostly used to provide more context and information to support requests.

Audit access can be performed by using the CLI in order to extract logs related to UI or API access. For instance, to extract audit logs from the last hour use the following commands:

• UI Logs

akamas logs --no-pagination -S kong -f -1h

• API Logs

akamas logs --no-pagination -S kong -f -1h

Notice: to visualize the system logs unrelated to the execution of workflows bound to workspaces, you need an account with administrative privileges.

Storing audit logs into files

To ease the integration with external logging systems, Akamas can be configured to store access logs into files. To enable this feature you should:

1. Create a logs folder next to the Akamas docker-compose.yml file

2. Edit the docker-compose.yml file by modifying the line FILE_LOG: "false" to FILE_LOG: "true"

3. If Akamas is already running issue the following command

docker compose up -d logstash

otherwise, start Akamas first.

When the user interacts with the UI or the API Akamas will report detailed access logs both on the internal database and in a file in the logs folder. To ease log rolling and management every day Akamas will create a new file named according to the pattern access-%{+YYYY-MM-dd}.log.

The following sections describe the procedure to upgrade your Akamas instance.

Docker compose Configuration

To start with the upgrade, on the Akamas server navigate to the same folder where the docker-compose.yml and .env file are stored (see section Get Akamas Docker artifacts). Now you can download the latest version compose file:

mv docker-compose.yml docker-compose.yml.bak
curl -O https://s3.us-east-2.amazonaws.com/akamas/compose/3.3.1/docker-compose.yml

You can point to a specific version. As an example to download the artifact for version 3.2.2:

curl -O https://s3.us-east-2.amazonaws.com/akamas/compose/3.2.2/docker-compose.yml

If the old docker-compose has been changed and it is still needed in the newer Akamas version, make sure to migrate such changes from docker-compose.yml.bak to the docker-compose.yml .

Ensure your .env file is up to date with the required variables, by comparing your version with the one at Configure Akamas environment variables.

Then log in to AWS with the following command:

aws ecr get-login-password --region us-east-2 | docker login --username AWS --password-stdin 485790562880.dkr.ecr.us-east-2.amazonaws.com

If the login succeeds, then you can start the upgrade by running:

docker compose up -d

Wait for a few minutes and check the Akamas services are running the command:

akamas status -d

The expected output should be like the following (repeat the command after a minute or two if the last line is not "OK" as expected):

Checking Akamas services on http://localhost:8000
service       status
=========================
analyzer      UP
campaign      UP
metrics       UP
optimizer     UP
orchestrator  UP
system        UP
telemetry     UP
license       UP
log           UP
users         UP
OK

Akamas allows dumping log entries from a specific service, workspace, workflow, study, trial, and experiment, for a specific timeframe and at different log levels.

Akamas CLI for logs

Akamas logs can be dumped via the following CLI command:

akamas log

This command provides many filters which can be retrieved with the following command:

akamas log --help

which should return

Usage: akamas log [OPTIONS] [MESSAGE]

  Show Akamas logs

Options:
  -d, --debug                     Show extended error messages if present.
  --page-size INTEGER             Number of log lines to be retrieved NOTE:
                                  This argument is mutually exclusive with
                                  arguments: [dump, no_pagination].
  --no-pagination                 Disable pagination and print all logs NOTE:
                                  This argument is mutually exclusive with
                                  arguments: [dump, page_size].
  --dump                          Print the logs without pagination and
                                  formatting NOTE: This argument is mutually
                                  exclusive with arguments: [page_size,
                                  no_pagination].
  -f, --from [%Y-%m-%d|%Y-%m-%dT%H:%M:%S|%Y-%m-%d %H:%M:%S|%Y-%m-%dT%H:%M:%S.%f|%Y-%m-%d %H:%M:%S.%f|[-]nw|[-]nd|[-]nh|[-]nm|[-]ns]
                                  The start timestamp of the logs
  -t, --to [%Y-%m-%d|%Y-%m-%dT%H:%M:%S|%Y-%m-%d %H:%M:%S|%Y-%m-%dT%H:%M:%S.%f|%Y-%m-%d %H:%M:%S.%f|[-]nw|[-]nd|[-]nh|[-]nm|[-]ns]
                                  The end timestamp of the logs
  -s, --study TEXT                UUID or name of the Study
  -e, --exp INTEGER               Number of the experiment
  --trial INTEGER                 Number of the trial
  -y, --system TEXT               UUID or name of the System
  -W, --workflow TEXT             UUID or name of the Workflow
  -l, --log-level TEXT            Log level
  -S, --service TEXT              Akamas service
  --without-metadata              Hide metadata
  --sorting [ASC|DESC]            Sorting order of the timestamps
  -ws, --workspace TEXT           UUID or name of the Workspace to visualize.
                                  When empty, system logs will be returned
                                  instead
  --help                          Show this message and exit.

For example, to get the list of the most recent Akamas errors:

akamas log -l ERROR

which should return something similar to:

       timestamp                         system                  provider    service                                                                                   message
==============================================================================================================================================================================================================================================================
2022-05-02T15:51:26.88    -                                      -          airflow     Task failed with exception
2022-05-02T15:51:26.899   -                                      -          airflow     Failed to execute job 2 for task Akamas_LogCurator_Task
2022-05-02T15:56:29.195   -                                      -          airflow     Task failed with exception
2022-05-02T15:56:29.215   -                                      -          airflow     Failed to execute job 3 for task Akamas_LogCurator_Task
2022-05-02T16:01:55.587   -                                      -          license     2022-05-02 16:01:47.426 ERROR 1 --- [           main] c.a.m.utils.rest.RestHandlers            :  has failed with returning a response:
                                                                                        {"httpStatus":400,"timestamp":"2022-05-02T16:01:47.413638","error":"Bad Request","message":"The following metrics: 'spark.spark_application_duration' were not found
                                                                                        in any of the components of the system 'analytics_cluster'","path":null}
2022-05-02T16:01:55.587   -                                      -          license     2022-05-02 16:01:47.434 ERROR 1 --- [           main] c.a.m.MigrationApplication               : Unable to complete operation. Mode: RESTORE. Cause: A request to a
                                                                                        downstream service CampaignService has failed: 400 : [{"httpStatus":400,"timestamp":"2022-05-02T16:01:47.413638","error":"Bad Request","message":"The following
                                                                                        metrics: 'spark.spark_application_duration' were not found in any of the components of the system 'analytics_cluster'","path":null}]
2022-05-02T16:01:55.678   -                                      -          license     2022-05-02 16:01:47.434 ERROR 1 --- [           main] c.a.m.MigrationApplication               : Unable to complete operation. Mode: RESTORE. Cause: A request to a
                                                                                        downstream service CampaignService has failed: 400 : [{"httpStatus":400,"timestamp":"2022-05-02T16:01:47.413638","error":"Bad Request","message":"The following
                                                                                        metrics: 'spark.spark_application_duration' were not found in any of the components of the system 'analytics_cluster'","path":null}]
2022-05-02T16:01:55.678   -                                      -          license     2022-05-02 16:01:47.426 ERROR 1 --- [           main] c.a.m.utils.rest.RestHandlers            :  has failed with returning a response:
                                                                                        {"httpStatus":400,"timestamp":"2022-05-02T16:01:47.413638","error":"Bad Request","message":"The following metrics: 'spark.spark_application_duration' were not found
                                                                                        in any of the components of the system 'analytics_cluster'","path":null}
2022-05-02T16:12:10.261   -                                      -          license     2022-05-02 16:05:53.209 ERROR 1 --- [           main] c.a.m.services.CampaignService           : de9f5ff9-418e-4e25-ae2c-12fc8e72cafc
2022-05-02T16:32:07.216   -                                      -          license     2022-05-02 16:31:37.330 ERROR 1 --- [           main] c.a.m.services.CampaignService           : 06c4b858-8353-429c-bacd-0cc56cc44634
2022-05-02T16:38:18.522   -                                      -          campaign    Internal Server Error: Object of class [com.akamas.campaign_service.entities.campaign.experiment.Experiment] with identifier
                                                                                        [ExperimentIdentifier(workspace=ac8481d3-d031-4b6a-8ae9-c7b366f027e8, study=de9f5ff9-418e-4e25-ae2c-12fc8e72cafc, id=2)]: optimistic locking failed; nested exception
                                                                                        is org.hibernate.StaleObjectStateException: Row was updated or deleted by another transaction (or unsaved-value mapping was incorrect) :
                                                                                        [com.akamas.campaign_service.entities.campaign.experiment.Experiment#ExperimentIdentifier(workspace=ac8481d3-d031-4b6a-8ae9-c7b366f027e8,
                                                                                        study=de9f5ff9-418e-4e25-ae2c-12fc8e72cafc, id=2)]

This guide introduces the optimization process and methodology with Akamas and then provides a step-by-step description of how to prepare, run and analyze Akamas optimization studies:

• General optimization process

• Preparing optimization studies

• Running optimization studies

and also provides some technology-specific guidelines and examples on:

• Guidelines for choosing optimization parameters

• Guidelines for defining optimization studies

The following guide uses the same chart repository and helm release names. Before starting the upgrade, you may find it helpful to look at the section Useful Commands.

Online

Start by updating the local chart repository:

helm repo update akamas

Start online upgrade

Ensure your kubectl configuration points to the namespace where Akamas is installed or specify it with the --namespace parameter. To start the upgrade to the latest version:

helm upgrade akamas akamas/akamas

You can specify an older chart version using the --version parameter. Refer to Listing Akamas chart versions for discovering the published chart versions.

If you need to specify a different Values file from the latest installation, start from the last one used. If you do not have it stored, it can be retrieved as specified in Retrieving the Values file.

Offline

Before starting the upgrade, check Configure the registry to add new docker images.

If you can not reach helm.akamas.io from the machine where the installation will be run, run the following commands from another client (see the installation guide for a full explanation).

helm repo update akamas
helm pull akamas/akamas

Then, you can start the upgrade in the same way as for the Online version. If you are using the downloaded chart package, transfer the package and replace akamas/akamas with the downloaded tgz archive.

Useful commands

Listing Akamas chart versions

Akamas' versions can be listed by running the following command:

helm search repo akamas/akamas --versions

It is always suggested to install and upgrade to the latest chart version. The App Version field refers to the Akamas version. To ease the release process multiple chart versions may refer to the same App Version.

Retrieving the Values file

In case you do not have access to the Values file used during the last installation/upgrade, you can still get it by running:

helm get values akamas -o yaml > akamas-values.yaml

Such a command is useful only if you need to change some of the parameters during the upgrade, otherwise the old Values file is kept by Helm.

External tools

You can use any monitoring tool to check the availability of the Akamas instance.

Checking Akamas services

To check the status of the Akams services please run akamas status -d to identify which service is not able to start up correctly

Here is an example of output:

Checking Akamas services on http://localhost:8000
 service	 status
=========================
analyzer       	UP
campaign       	UP
metrics        	UP
optimizer      	UP
orchestrator   	UP
system         	UP
telemetry      	UP
license        	UP
log            	UP
users          	UP
OK

This section covers some topics on how to manage the Akamas Server:

• Akamas logs

• Audit logs

• Install upgrades and patches

• Monitor the Akamas Server

• Backup & Recovery of the Akamas Server

Akamas server backup

The process of backing up an Akamas server can be divided in two parts, that is system backup and otherwise start Akamas. Backup can be performed in any way you see fit: they’re just regular files so you can use any backup tool.

System backup

System services are hosted on AWS ECR repo so the only thing that fully defines a working Akamas application is the docker-compose.yml file. Performing a backup of the Akamas application is as simple as copying this single file to your backup location. you may schedule any script that performs this weekly or at any frequency you see fit

User data backup

You may list all existing Akamas studies via the Akamas CLI command:

akamas list study

Then you can export all existing studies one by one via the CLI command

akamas export study <UUID>

where UUID is the UUID of a single study. This command exports into a single archive file (tar.gz). These archive files can be backed up to your favorite backup folder.

Akamas server recovery

Akamas server recovery involves recovering the system backup, restarting the Akamas service then re-importing the studies.

System Restore

To restore the system you must recover the original docker-compose.yml then launch the command

docker compose up &

from the folder where you placed this YAML file and then wait for the system to come up, by checking it with the command

akamas status -d

User data restore

All studies can be re-imported singularly with the CLI command (referring to the correct pathname of the archive):

akamas import study archive.tgz

This page provides a walkthrough to configure Google as an external identity provider for Akamas users.

Configure the App registration

To integrate Akamas with your Google Workspace, you first need a project with a dedicated OAuth client. Log in to your Google Developer Console, and navigate to the "Credentials" page of "API & Services".

Configure the Consent Screen

If the "Credentials" page displays a warning box reminding you to configure the consent screen, you first need to create an app. Click the enclosed button to start the wizard.

Follow the wizard to configure the consent screen according to your company's policies. For more details, refer to Configure the OAuth consent screen on the official documentation.

Once the configuration is complete, return to the "Credentials" page.

Create the OAuth client

Click the "Create Credentials" link on top, and select "OAuth Client ID".

Configure the client as follows:

• "Application Type": select "Web application"

• "Name": populate with the name of the new client

• "Authorized redirect URIs": leave it blank, as you will fill it in a later step

Once you click "Create" the console will show you a confirmation pop-up containing the client's configuration. Note the Client ID and the Client Secret.

Create the Identity provider

Access the Identity providers section for the "akamas" realm in the Keycloak administration console, as described on the page Configure an external identity provider, and select "Google" to start creating the new provider.

Configure the following fields using the values from the OAuth client you just created:

• "Client ID": fill in the id of the client

• "Client Secret": fill in the secret of the client

To complete the configuration, note the "Redirect URI" value and click "Add".

Complete the app registration

Back to the Google Developer Console, on the "Credentials" page, open the newly created client and add the URI from the previous step to the list of "Authorized redirect URIs".

Configure the default Akamas roles

The final setup step is to instruct Akamas to associate the default roles with the users automatically. This way, users will be added to the default workspace with read and write permissions the first time they log in.

On the Keycloak console, on the provider's details page, navigate to "Mappers":

Now, add the following configurations.

User role

• Name: User role

• Mapper type: Hardcoded role

• Role: USER

Default Workspace Read

• Name: Default Workspace Read

• Mapper type: Hardcoded role

• Role: WS_ac8481d3-d031-4b6a-8ae9-c7b366f027e8_R

Default Workspace Write

• Name: Default Workspace Write

• Mapper type: Hardcoded role

• Role: WS_ac8481d3-d031-4b6a-8ae9-c7b366f027e8_W

Test the integration

Visit the installation's login page to check that the new authentication method is displayed and works correctly.

After identifying the components that are required to model a system, the following step is to model each identified key component.

Akamas provides the corresponding component types for their specific technology (and possibly version) and optimization packs describing all the tunable parameters and metrics of interest. The full list of Akamas optimization packs is available on the optimization packs page of the Akamas reference guide.

The Component template section of the reference guide describes the template required to define a system component, while the commands for creating a system component are listed on the Resource Management command page.

While the optimization process does not necessarily require component types and optimization packs to be defined, it is recommended to leverage this construct to facilitate modularization and reuse.

This is possible as the Akamas optimization pack model is extensible: custom optimization packs can be easily created without any programming to allow Akamas optimization capabilities to be applied to virtually any technology.

The Creating custom optimization pack page describes how to create a new optimization pack (possibly by reusing an already existing one) while the Component Type template page in the Akamas reference guide describes how to define a custom component type (if required).

Notice that optimization packs, even if provided out-of-the-box by Akamas, need to be installed (as described on the Managing optimization packs page), in case they have not yet been used before in the Akamas installation, by other users. Indeed, optimization packs are global resources that are shared across all the workspaces on the same Akamas installation.

Whether out-of-the-box or custom, before being used optimization packs need to be installed on an Akamas installation before being used.

Since optimization packs are global resources that are shared across all the workspaces on the same Akamas installation, an account with administrative privileges is required to manage them.

Optimization packs that are not yet installed are displayed as grayed out in the Akamas UI (this is the case for the AWS and Docker packs in the following figure).

An Akamas installation comes with the latest optimization packs already loaded in the store and is able to check the central repository for updates.

Installing

There are two ways of installing an optimization pack:

• online installation - this is the general case when the optimization pack is already in the store

• offline installation - this may apply to custom optimization packs available as a JSON file (refer to the Creating custom optimization pack page)

Only in the first case, an optimization pack can be installed from the UI. See here below the command line commands to get an optimization pack installed.

Online installation

Execute the following command by specifying the name of the optimization pack that is already available in the store:

akamas install optimization-pack OPTIMIZATION_PACK_NAME

Offline installation

The following command describes how to download the file descriptor https://akamas.s3.us-east-2.amazonaws.com/optimization-packs/Linux/1.3.0/Linux_1-3-0.json related to the version 1.3.0 of the Linux optimization pack:

curl -O https://akamas.s3.us-east-2.amazonaws.com/optimization-packs/Linux/1.3.0/Linux_1-3-0.json akamas install optimization-pack Linux_1-3-0.json

Execute the following command to install an optimization pack by specifying the name of the optimization pack and the full path to the JSON descriptor file:

akamas install optimization-pack PATH_TO_JSON_DESCRIPTOR

Forcing installation

When installing an optimization pack, the following checks are executed to identify potential clashes with already existing resources:

• name of the optimization pack

• metrics

• parameters

• component types

• telemetry providers

In case one of those checks is positive (i.e. a clash exists), the installation failed and a message notifies that a "force" option needs to be used to get the optimization pack installed anyway

akamas install -f optimization-pack OPTIMIZATION_PACK_NAME

Please be aware that when forcing the installation of an optimization pack, Akamas replaces (or merges) all the conflicting resources, except that if there is at least one custom resource, the installation is stopped. In this case, the custom resource needs to be manually removed first in order to proceed.

Uninstalling

The following command uninstalls an optimization pack

akamas uninstall --force OPTIMIZATION_PACK_NAME

Notice that this also deletes all the components built using that optimization pack.

Updating

In case a new optimization pack needs to be installed from a descriptor, the procedure is the following:

• uninstall the optimization pack

• remove the old version of the optimization pack descriptor file from the store container;

• install the new optimization pack with the new JSON descriptor

The very first preparatory step is to model the representing an application or a service that needs to be optimized (also known as the optimization target).

Modeling a system translates into identifying the representing the key technology elements to be included in the optimization. Each component is associated with a set of tunable , i.e. configurable properties that impact the performance, efficiency, or reliability of the system, and with a set of , i.e. measurable properties that are used to evaluate the performance, efficiency, or reliability of the system. Typically, key system components are identified by considering which elements and their parameters need to be tuned.

The following figure shows a system corresponding to a Java-based application, where the Java Virtual Machine (JVM) and Kubernetes containers have been identified as key components.

As shown in this figure, a supported component is the "web application", representing the end user perspective of the modeled system (e.g. response time). As expected, this component type only provides measured metrics and no tunable parameters.

Best Practices

Properly modeling the application or service to be optimized by identifying the components and their parameters to tune is the first important step in the optimization process. Some best practices are described here below.

Modeling only relevant components

When defining the system and its components, it is convenient to focus only on those components that are either providing tunable parameters or key metrics (or KPIs).

Key metrics are those used to:

• support the analysis of the optimization results, as metrics that are useful to measure the impact of parameter tuning on the performance, efficiency, or reliability of the system. For example, a Linux OS component could be used to assess the impact of the optimization on the system-level metrics such as CPU utilization.

Please note that the metrics used to define the optimization goal and constraints are mandatory as they are used by the Akamas AI engine to validate and score each tested configuration against the goal. Other metrics that are not related to the optimization goal and constraints can be considered optional from a pure optimization implementation perspective.

When defining the optimization study, it is always possible to select which parameters and metrics to consider, thus which components are modeled in the system. Therefore, a system could be modeled by all components that at some point are going to be optimized, even if not used in the current optimization study. However, the recommended approach is to model the system only with components whose parameters (and relevant metrics) are to be tuned by the current study.

Reusing systems whenever possible

Whenever possible, it is recommended to model systems and their components by considering how these could be reused for multiple optimization studies in different contexts.

For example, it might be useful to create a simple system containing only one component (e.g. the JVM) for a first optimization study. A new system might then be created to include other components (e.g. the application server) for more advanced optimization studies.

Modeling systems with horizontal scalability

A typical optimization target is a cluster, i.e. a system made of multiple instances that provide horizontal scalability (e.g. a Kubernetes deployment with several replicas). In this scenario, all the instances are supposed to be identical both from a code and configuration perspective. In this scenario, the recommended approach is to create only one component that represents a generic instance of the cluster. This way, all the instances will be tuned in exactly the same way.

Notice that in order for this approach to work correctly, it is also important to verify that the cluster is correctly monitored by the telemetry providers. Depending on the telemetry technology in use, the clustered system may be presented as either a single entity, with aggregated metrics (e.g. a Kubernetes deployment with the total CPU usage of all the replica pods), or as multiple entities, each corresponding to the different instances in the cluster:

• in case aggregated metrics are provided by the telemetry provider for the cluster, these metrics can be simply assigned to the component modeling the whole cluster;

• in case only instance-level metrics are made available by the telemetry provider, telemetry instances need to be configured in Akamas so as to aggregate the metrics of the cluster instances (e.g. averaging CPU utilization, summing memory usage, etc.), depending on how each specific metric is expected to be used in the goal and constraints or in the study results.

To create a custom optimization pack, the following fixed directory structure and several YAML manifests need to be created.

Optimization pack directory structure

Optimization pack manifest

The optimizationPack.yaml file is the manifest of the optimization pack to be created, which should always be named optimizationPack and have the following structure:

where:

Component types

The component-types directory should contain the manifests of the component types to be included in the optimization pack. No particular naming constraint is enforced on those manifests.

Metrics

The metrics directory should contain the manifests of the groups of metrics to be included in the optimization pack. No particular naming constraint is enforced on those manifests.

Parameters

The parameters directory should contain the manifests of the groups of parameters to be included in the optimization pack. No particular naming constraint is enforced on those manifests.

Telemetry providers

The telemetry-providers directory should contain the manifests of the groups of parameters to be included in the optimization pack. No particular naming is enforced on those manifests.

Building optimization pack descriptor

The following command need to be executed in order to produce the final JSON descriptor:

After modeling the system and its components, the following step (see the following figure) is to ensure that all the metrics that are required to define goals and constraints and analyze the behavior of the target system can be collected from one of the available data sources available in the environment, that in Akamas are called .

Akamas provides a number of out-of-the-box telemetry providers, including industry-standard monitoring platforms (e.g. Prometheus or Dynatrace), performance testing tools (e.g. LoadRunner or JMeter), or simple CSV files. The section lists all the out-of-the-box telemetry providers and how to get them integrated by Akamas, while the section describes the mapping of the specific data source metrics to Akamas metrics).

Since several instances of a data source type might be available, the specific data source instance needs to be specified, that is a corresponding needs to be defined for the modeled system and its components.

Telemetry Providers are shared across all the workspace in the same Akamas installation and require an account with administrative privileges to manage them. Any number of telemetry instances (even of the same type) can be specified. For example, the following figure shows two Prometheus telemetry instances associated with the Adservice system.

Best Practices

The following sections provide guidelines on how to create telemetry instances.

Verify metrics provided by the telemetry provider

A seemingly obvious, yet fundamental, best practice when choosing a telemetry provider is to check whether the required metrics:

• are supported by the original data source or can be added (e.g. as it is in the case of Prometheus)

• are available and can be effectively gathered in the specific implementation

• are supported by the telemetry provider itself or whether it needs to be extended (this is the case for a Prometheus telemetry provider ) as in the case of custom metrics such as those made available by the application itself

A workflow for a automates all the actions required to interface the configuration management. Notice that metrics collection is an implicit action that does not need to be coded as part of the workflow.

More in detail, a typical workflow includes the following types of tasks:

• Applying the configuration, by preparing and then applying the parameter configuration that has been recommended and/or approved to the target environment - this may require interfacing configuration management tools or pushing configuration to a repository

Depending on the complexity of the system, the workflow might be composed by multiple actions of the same type, each operating on separate components of the target system.

For both offline and live optimization studies, it is possible to define how to identify the time windows that Akamas needs to consider for assessing the result of an experiment. Defining a windowing policy helps achieve reliable optimizations by excluding metrics data points that should not influence the score of an experiment.

The following two windowing policies are available:

• Trim windowing: discards the initial and final part of an experiment - e.g. to exclude warm-up and tear-down phases - trim windowing policy is the default (with entire interval selection whether no trimming is specified)

• Stability windowing: discard those parts that do not correspond to the most stable window - this leverages the Akamas features of automatically identifying the most stable window based on the user-specified specified criteria

Best Practices

The following sections provide general best practices on how to define suitable windowing policy.

Define windowing based on the optimization goal

In order to make the optimization process fully automated and unattended, Akamas automatically analyzes the time series of the collected metrics of each experiment and calculates the experiment score (all the system metrics will also be aggregated).

Based on the optimization goal, it is important to instruct Akamas on how to perform this experiment analysis, in particular, by also leveraging Akamas windowing policies.

For example, when optimizing an online or transactional application, there are two common scenarios:

1. Increase system performance (i.e. minimize response time) or reduce system costs (i.e. decrease resource footprint or cloud costs) while processing a given and fixed transaction volume (i.e. a load test);

2. Increase the maximum throughput a system can support (i.e., system capacity) while processing an increasing amount of load (e.g. a stress test).

In the first scenario, a load test scenario is typically used: the injected load (e.g. virtual users) ramps up for a period, followed by a steady state, with a final ramp-down period. From a performance engineering standpoint, since the goal is to assess the system performance during the steady state, the warm-up and tear-down periods can be discarded. This analysis can be automated by applying a windowing policy of type "trim" upon creating the optimization study, which makes Akamas automatically compute the experiment score by discarding a configurable warm-up and tear-down period.

In the second scenario, a stress test is typically used: the injected load follows a ramp with increasing levels of users, designed to stress the system up to its limit. In this case, a performance engineer is most likely interested in the maximum throughput the system can sustain before breaking down (possibly while matching a response time constraint). This analysis can be automated by applying a windowing policy of type "stability", which makes Akamas automatically compute the experiment score in the time window where the throughput was maximized but stable for a configurable period of time.

When optimizing a batch application, windowing is typically not required. In such scenarios, a typical goal is to minimize batch duration or aggregate resource utilization. Hence, there is no need to define any windowing policy: by default, the whole experiment timeframe is considered.

Finding an effective stability window

Setting up an effective stability window requires some knowledge of the test scenario and the variability of the environment.

As a general guideline it is recommended to run a baseline study with a stability window set to a low value, such as a value close to 0 or half of the expected mean of the metric, and then to inspect the results of the baseline to identify which window has been identified and update the standard deviation threshold accordingly. When using a continuous ramp the test has no plateaus, so the standard deviation threshold should be a bit higher to account for the increment of the traffic in the windowing period. On the contrary, when running a staircase test with many plateaus, the standard deviation can be smaller to identify a period of time with the same amount of users.

Applying the standard deviation filter to very stable metrics, such as the number of users, simplifies the definition of the standard deviation threshold but might hide some instability of the environment when subject to constant traffic. On the other hand, applying the threshold to a more direct measure of the performance, such as the throughput, makes it easier to identify the stability period of the application but might require more baseline experiments to identify the proper threshold value. The logs of the scoring phase provide useful insights into the maximum standard deviation found and the number of candidate windows that have been identified given a threshold value, which can be used to refine the threshold in a few baseline experiments.

A workflow for an automates all the actions required to interface the configuration management and load testing tools (see the following figure) at each experiment or trial. Notice that metrics collection is an implicit action that does not need to be coded as part of the workflow.

More in detail, a typical workflow includes the following types of tasks:

• Preparing the application, by executing all cleaning or reset actions that are required to prepare the load testing phase and ensuring that each experiment is executed under exactly the same conditions - for example, this may involve cleaning caches, uploading test data, etc

• Applying the configuration, by preparing and then applying the parameter configuration under test to the target environment - this may require interfacing configuration management tools or pushing configuration to a repository, restarting the entire application or some of its components to ensure that some parameters are effectively applied, and then checking that after restarting the application is up & running before the workflow execution continues, and checking whether the configuration has been correctly applied

• Applying the workload, by launching a load test to assess the behavior of the system under the applied configuration and synthetic workload defined in the load testing scenarios - of course, a preliminary step is to design a load testing scenario and synthetic workload that ensures that optimized configurations resulting from the offline optimization can be applied to the target system under the real or expected workload

Failing workflows

A workflow interrupts in case any of its steps does. A failing workflow causes the experiment or trial to fail. This should be considered as a different situation than a specific configuration not matching optimization constraints or causing the system under test to fail to run. For example, if the amount of max memory configured was too low, the application may fail to start.

When an experiment fails, the Akamas AI engine takes this information into account and thus learns that that parameter configuration was bad. This way, the AI engine automatically tries to avoid the regions of the parameter space which can lead to low scores or failures.

Best Practices

Creating effective workflows is essential to ensure that Akamas can automatically identify the optimal configuration in a reliable and efficient way. Some best practices on how to build robust workflows are described here below.

Reusing workload as much as possible

Since Akamas workflows are first-class entities that can be used by multiple studies, it might be useful to avoid creating (and maintaining) multiple workflows and instead define workflows that can be easily reused, by factoring all differences into specific action parameters.

Of course, this general guideline should be balanced with respect to other requirements, such as avoiding potential conflicts due to different teams modifying the same workload for different uses and potentially impacting optimization results.

Building robust workflows

Akamas takes into account the exit code of each of the workflow tasks, and the whole workflow fails if a task exits with an error. Therefore, the best practice is to make use of exit codes in each task, to ensure that task failures can only happen in case of bad parameter configuration.

For example, it is important to always check that the application has correctly started and is up and running (after a new configuration has been applied). This can be done by:

• including a workflow task that tests the application is up and running after the tasks where the configuration is applied;

• making sure that this task exits with an error in case the application has not correctly started (typically after a timeout).

Another example is when the underlying environment incurs issues during the optimization (e.g. a database might be mistakenly shut down by another team). As much as possible, all these environmental transient issues should be carefully avoided. Akamas also provides the ability to execute multiple task retries (default is twice, configurable) to compensate for these transient issues, provided they only last for a short time (the retry time and delay are also configurable).

Building workflows that ensure reproducible experiments

As for any other performance evaluation activity, Akamas experiments should be designed to be reproducible: if the same experiment (hence, the same parameter configuration) is executed multiple times (i.e. in multiple trials), the same performance results should be found for each trial.

Therefore, it is fundamental that workflows include all the necessary tasks to realize reproducible experiments. Particular care needs to be taken to correctly manage the system state across the experiments and trials. System state can include:

• Application caches

• Operating system cache and buffers (e.g. Linux filesystem page cache)

• Database tables that fill up during the optimization process

All experiments should always start with a clean and well-known state. If the state is not properly managed, it may happen that the performance of the system is observed to change (whether higher or lower) not because of the effect of the applied parameters, but due to other effects (e.g. warming of caches).

Best practices to consistently manage system state across experiments include:

• Restoring the system state at the beginning of each experiment - this may involve restarting the application, clearing caches, restoring DB tables, etc;

• Allowing for a sufficient warm-up period in the performance tests, so to ensure application performance has reached stability. See also the recommended best practices about properly managing warm-up periods in the following section about creating an optimization study.

Another common cause that can impact the reproducibility of experiments is an unstable infrastructure or environment. Therefore, it is important to ensure that the underlying infrastructure is stable and that no other workload that might impact the optimization results is running on it. For example, beware of scheduled system jobs (e.g. backups), automatic software updates or anti-virus systems that might not explicitly be considered as part of the environment but that may unexpectedly alter its performance behavior.

Taking into account workflow duration

When designing workflows, it is important to take into account the potential duration of their tasks. Indeed, the task duration impacts the duration of the overall optimization and might impact the ability to execute a sufficient number of experiments within the overall time interval or specific time windows allowed for the optimization study.

Typically, the longest task in a workflow is the one related to applying workload (e.g. launching a load test or a batch job): such tasks can last for dozens of minutes if not hours. However, a workflow may also include other ancillary tasks that may provide nontrivial contributions to the task durations (e.g. checking the status to ensure that the application is up & running).

Making workflows fail fast

As general guidance, it is better to fail fast by performing quick checks executed as early as possible. For example, it is better to do a status check before launching a load test instead of possibly waiting for it to complete (maybe after 1h) just to discover that the application did not even start.

Preparing an optimization study requires several steps, as illustrated by the following figure:

and described in the following sections:

Notice that while these steps apply to both offline optimization studies and live optimization studies, some of these steps are different depending on which optimization is being prepared.

The final preparatory step before running a study is to actually create the study, which also requires several substeps.

Most of the substeps are common for both a and an , even if they might need to be conceived differently in these two different contexts:

Offline optimization studies

For offline optimization studies, there are some additional (optional) steps:

Live optimization studies

For live optimization studies, there are some additional steps - including a mandatory one:

After modeling the system and its components and ensuring that appropriate telemetry instances are defined, the following step (see the following figure) is to define a .

A workflow automates all the tasks to be executed in sequence (see the following figure) during the optimization study, in particular those leveraging integrations with external entities, such as telemetry providers or configuration management tools. Akamas provides a number of general-purpose and specialized workflow operators (see page).

The first fundamental step in creating a study is to define the study . While this step might be perceived as somewhat straightforward (e.g. constraints could be simply translated from SLOs already in place), defining the optimization goal really requires carefully balancing complexity and effectiveness, also as part of the general (iterative) optimization process. Please also read the section here below.

In general, any performance engineering, tuning, and optimization activity involves complex tradeoffs among different - and potentially conflicting - goals and system performance metrics, such as:

• Maximizing the business volume an application can support, while not making the single transaction slower or increasing errors above a desired threshold

• Minimizing the duration of a batch processing task, while not increasing the cloud costs by more than 20% or using more than 8 CPUs

Akamas support all these (and other) scenarios by means of the optimization goal, that is the single metric or the formula combining multiple metrics that have to be either minimized or maximized, and one or more constraints among metrics of the system.

In general, constraints can be defined as either absolute constraints (e.g. app.response_time < 200 ms) or as relative constraints with respect to a baseline (e.g. app_response_time < +20% of the baseline), that is the current configuration in place, typically corresponding to the very first experiment in an offline optimization study which. Therefore, relative constraints are only applicable to offline optimization studies, while absolute constraints are applicable to both absolute and relative constraints.

Please notice that when defining constraints for an optimization study, it is required to also include those constraints listed in the Constraints section of the respective Optimization Packs which express internal constraints among parameters. For example, in case OpenJDK 11 components are to be tuned, the reference section is .

The page of the in the reference guide describes the corresponding structures. For offline optimization studies only, the Akamas UI allows the optimization goal and constraints to be defined as part of the visual procedure activated by the "Create a study" button (see the following figure).

Please notice that any experiment that does not respect the constraints is marked by Akamas as failed, even if correctly executed. The reason for this failure can be inspected in the experiment status. Similarly to workflow failures (see below), the Akamas AI engine automatically takes any failure due to constraint violations into account when searching the optimization space to identify the parameter configurations that might improve the goal metrics while matching constraints.

Best Practices

There are no general guidelines and best practices on how to best define goals & constraints, as this is where experience, knowledge, and processes meet.

After defining the goal and its constraints, the following substep in creating an optimization study is specifying the optimization and . In particular, selecting the parameters that are going to be tuned to optimize the system is a critical decision that requires carefully balancing complexity and effectiveness. As for goals & constraints, also this step may require adopting an iterative approach. See also the section here below.

The and pages of the section in the reference guide describe how to define the corresponding structure. For offline optimization studies only, the Akamas UI allows the parameters and metrics to be defined as part of the visual procedure activated by the "Create a study" button (see the following figure).

As illustrated by the previous and following figures, during this step is also possible to edit the range of values associated with each optimization parameter with respect to the default domain provided by either the original or custom optimization pack in use for the respective technology.

Parameter rendering

By default, all parameters specified in the parameters selection of a study are applied ("rendered"). Akamas allows specifying which configuration parameters should be applied in the optimization steps. More precisely:

• parameter rendering is available at the step level for baseline, preset, and optimize steps

• parameter rendering is not available for bootstrap steps (bootstrapped experiments are not executed)

This feature can be useful to deal with the different strategies through which applications and systems accept configuration parameters.

Best Practices

The following sections provide some best practices on how to best approach the step of defining optimization parameters. .

Configure parameters domains based on environment specs

Since the parameter domain defines the range of values that the Akamas AI engine can assign to the parameter, when defining the system parameters to be optimized, it is important to review the parameter domains and adjust them based on the system characteristics of the target system, environment and best practices in place.

Akamas optimization packs already provide parameter domains that are correct for most situations. For example, the OpenJDK 11 JVM gcType is a categorical parameter that already includes all the possible garbage collectors that can be set for this JVM version.

For other parameters, there are no sensible default domains as they depend on the environment. For example, the OpenJDK 11 maxHeapSize JVM parameter dictates how much memory the JVM can use. This obviously depends on the environment in which the JVM runs. For example, the upper bound might be 90% of the memory of the virtual machine or container in which the JVM runs.

Configure parameter constraints based on Optimization Pack best practices

Depending on the specific technology under optimization, the configuration parameters may have relationships among themselves. For example, in a JVM the newSize parameter defines the size of a region of the JVM heap, and hence its value should be always less than the maxHeapSize parameter.

Akamas AI engine supports the definition of constraints among parameters as this is a frequent need when optimizing real-life applications.

It is important to define the parameter constraints when creating a new study. The optimization pack documentation provides guidelines on what are the most important parameter constraints for the specific technology.

When optimizing a new or custom technology, it may happen that some experiments fail due to unknown parameter constraints being violated. For example, the application may fail to start and only by analyzing the application error logs, the reason for the failure can be understood. For a Java application, the JVM error message (e.g. "new size cannot be larger than max heap size") could provide useful hints. This would reveal that some constraints need to be added to the parameter constraints in the study.

While the Akamas AI engine has been designed to learn from failures, including those due to relationships among parameters that were not explicitly set as constraints, setting parameter constraints may help avoid unnecessary failures and thus speed up the optimization process.

While the optimization goal drives the Akamas AI toward optimal configurations, there might be other sub-optimal configurations of interest in case they do not simply match the optimization constraints but might also improve on some Key Performance Indicators (KPIs).

For example:

• for a Kubernetes microservice Java-based application, a typical optimization goal is to reduce the overall (infrastructure or cloud) cost by tuning both Kubernetes and JVM parameters while keeping SLOs in terms of application response time and error rate under control

• among different configurations that provide similar cost reduction in addition to matching all SLOs, a configuration that would also significantly cause the application response time might be worth considering with respect to an optimal configuration that does not improve on this KPI

Akamas automatically considers any metric referred to in the defined optimization goal and constraints for an offline optimization study as a KPI. Moreover, any other metrics of the system component can be specified as a KPI for an offline optimization study.

The page of the section in the reference guide describes how to define the corresponding structure. Specifying the KPIs can be done while first defining the study or from the Akamas UI, at either study creation time or afterward (see the following figures).

Once KPIs are defined, Akamas will represent the results of the optimization in the Insights section of the Akamas UI. Moreover, the corresponding suboptimal configuration associated with a specific KPI is highlighted in the Akamas UI by a textual badge "Best <KPI name>".

Once all the preparatory steps for creating a study are done, running a study is straightforward: An optimization study can be started from either the Akamas UI (see the following figures) or the command line (refer to the page).

Before actually running an optimization study, it is highly recommended to read the following sections:

• or

Offline optimization studies

This can be useful for multiple reasons, including the case of an error (e.g. a misconfigured workflow) that requires "restarting" the study.

Live optimization studies

For live optimization studies, it is possible to stop a study and restart it. However, please notice that this is an irreversible action, that would delete all the executed experiments, so basically, restarting a live study means starting it from scratch.