Akamas Docs
3.1.2
3.1.2
  • How to use this documentation
  • Getting started with Akamas
    • Introduction to Akamas
    • Licensing
    • Deployment
      • Cloud Hosting
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    • Maintenance & Support (M&S) Services
      • Customer Support Services
      • Support levels for Customer Support Services
      • Support levels for software versions
      • Support levels with Akamas 3.1
  • Installing Akamas
    • Akamas Architecture
    • Prerequisites
      • Hardware Requirements
      • Software Requirements
      • Network requirements
    • Install Akamas dependencies
    • Install the Akamas Server
      • Online installation mode
        • Online installation behind a Proxy server
      • Offline installation mode
      • Changing UI Ports
      • Setup HTTPS configuration
    • Install the Akamas CLI
      • Setup the Akamas CLI
      • Verify the Akamas CLI
      • Initialize Akamas CLI
      • Change CLI configuration
    • Verify the Akamas Server
    • Install the Akamas license
    • Manage anonymous data collection
    • Install an Akamas Workstation
    • Troubleshoot install issues
    • Manage the Akamas Server
      • Akamas logs
      • Audit logs
      • Install upgrades and patches
      • Monitor the Akamas Server
      • Backup & Recover of the Akamas Server
  • Using Akamas
    • General optimization process and methodology
    • Preparing optimization studies
      • Modeling systems
      • Modeling components
        • Creating custom optimization packs
        • Managing optimization packs
      • Creating telemetry instances
      • Creating automation workflows
        • Creating workflows for offline studies
        • Performing load testing to support optimization activities
        • Creating workflows for live optimizations
      • Creating optimization studies
        • Defining optimization goal & constraints
        • Defining windowing policies
        • Defining KPIs
        • Defining parameters & metrics
        • Defining workloads
        • Defining optimization steps
        • Setting safety policies
    • Running optimization studies
      • Before running optimization studies
      • Analyzing results of offline optimization studies
        • Optimization Insights
      • Analyzing results of live optimization studies
      • Before applying optimization results
    • Guidelines for choosing optimization parameters
      • Guidelines for JVM (OpenJ9)
      • Guidelines for JVM layer (OpenJDK)
      • Guidelines for Oracle Database
      • Guidelines for PostgreSQL
    • Guidelines for defining optimization studies
      • Optimizing Linux
      • Optimizing Java OpenJDK
      • Optimizing OpenJ9
      • Optimizing Web Applications
      • Optimizing Kubernetes
      • Optimizing Spark
      • Optimizing Oracle Database
      • Optimizing MongoDB
      • Optimizing MySQL Database
      • Optimizing PostgreSQL
  • Integrating Akamas
    • Integrating Telemetry Providers
      • CSV provider
        • Install CSV provider
        • Create CSV provider instances
      • Dynatrace provider
        • Install Dynatrace provider
        • Create Dynatrace provider instances
      • Prometheus provider
        • Install Prometheus provider
        • Create Prometheus provider instances
        • CloudWatch Exporter
        • OracleDB Exporter
      • Spark History Server provider
        • Install Spark History Server provider
        • Create Spark History Server provider instances
      • NeoLoadWeb provider
        • Setup NeoLoadWeb telemetry provider
        • Create NeoLoadWeb provider instances
      • LoadRunner Professional provider
        • Install LoadRunner Professional provider
        • Create LoadRunner Professional provider instances
      • LoadRunner Enterprise provider
        • Install LoadRunner Enterprise provider
        • Create LoadRunner Enterprise provider instances
      • AWS provider
        • Install AWS provider
        • Create AWS provider instances
    • Integrating Configuration Management
    • Integrating Value Stream Delivery
    • Integrating Load Testing
      • Integrating NeoLoad
      • Integrating Load Runner Professional
      • Integrating LoadRunner Enterprise
  • Akamas Reference
    • Glossary
      • System
      • Component
      • Metric
      • Parameter
      • Component Type
      • Workflow
      • Telemetry Provider
      • Telemetry Instance
      • Optimization Pack
      • Goals & Constraints
      • KPI
      • Optimization Study
      • Offline Optimization Study
      • Live Optimization Study
      • Workspace
    • Construct templates
      • System template
      • Component template
      • Parameter template
      • Metric template
      • Component Types template
      • Telemetry Provider template
      • Telemetry Instance template
      • Workflows template
      • Study template
        • Goal & Constraints
        • Windowing policy
          • Trim windowing
          • Stability windowing
        • Parameter selection
        • Metric selection
        • Workload selection
        • KPIs
        • Steps
          • Baseline step
          • Bootstrap step
          • Preset step
          • Optimize step
        • Parameter rendering
    • Workflow Operators
      • General operator arguments
      • Executor Operator
      • FileConfigurator Operator
      • LinuxConfigurator Operator
      • WindowsExecutor Operator
      • WindowsFileConfigurator Operator
      • Sleep Operator
      • OracleExecutor Operator
      • OracleConfigurator Operator
      • SparkSSHSubmit Operator
      • SparkSubmit Operator
      • SparkLivy Operator
      • NeoLoadWeb Operator
      • LoadRunner Operator
      • LoadRunnerEnteprise Operator
    • Telemetry metric mapping
      • Dynatrace metrics mapping
      • Prometheus metrics mapping
      • NeoLoadWeb metrics mapping
      • Spark History Server metrics mapping
      • LoadRunner metrics mapping
    • Optimization Packs
      • Linux optimization pack
        • Amazon Linux
        • Amazon Linux 2
        • Amazon Linux 2022
        • CentOS 7
        • CentOS 8
        • RHEL 7
        • RHEL 8
        • Ubuntu 16.04
        • Ubuntu 18.04
        • Ubuntu 20.04
      • DotNet optimization pack
        • DotNet Core 3.1
      • Java-OpenJDK optimization pack
        • Java OpenJDK 8
        • Java OpenJDK 11
      • OpenJ9 optimization pack
        • IBM J9 VM 6
        • IBM J9 VM 8
        • Eclipse Open J9 11
      • NodeJS optimization pack
        • NodeJS
      • GO optimization pack
        • GO 1
      • Web Application optimization pack
        • Web Application
      • Docker optimization pack
        • Container
      • Kubernetes optimization pack
        • Kubernetes Pod
        • Kubernetes Container
        • Kubernetes Workload
        • Kubernetes Namespace
        • Kubernetes Cluster
      • WebSphere optimization pack
        • WebSphere 8.5
        • WebSphere Liberty ND
      • AWS optimization pack
        • EC2
        • Lambda
      • PostgreSQL optimization pack
        • PostgreSQL 11
        • PostgreSQL 12
      • Cassandra optimization pack
        • Cassandra
      • MySQL Database optimization pack
        • MySQL 8.0
      • Oracle Database optimization pack
        • Oracle Database 12c
        • Oracle Database 18c
        • Oracle Database 19c
        • RDS Oracle Database 11g
        • RDS Oracle Database 12c
      • MongoDB optimization pack
        • MongoDB 4
        • MongoDB 5
      • Elasticsearch optimization pack
        • Elasticsearch 6
      • Spark optimization pack
        • Spark Application 2.2.0
        • Spark Application 2.3.0
        • Spark Application 2.4.0
    • Command Line commands
      • Administration commands
      • User and Workspace management commands
      • Authentication commands
      • Resource management commands
      • Optimizer options commands
  • Knowledge Base
    • Setting up a Konakart environment for testing Akamas
    • Modeling a sample Java-based e-commerce application (Konakart)
    • Optimizing a web application
    • Optimizing a sample Java OpenJ9 application
    • Optimizing a sample Java OpenJDK application
    • Optimizing a sample Linux system
    • Optimizing a MongoDB server instance
    • Optimizing a Kubernetes application
    • Leveraging Ansible to automate AWS instance management
    • Guidelines for optimizing AWS EC2 instances
    • Optimizing a sample application running on AWS
    • Optimizing a Spark application
    • Optimizing an Oracle Database server instance
    • Optimizing an Oracle Database for an e-commerce service
    • Guidelines for optimizing Oracle RDS
    • Optimizing a MySQL server database running Sysbench
    • Optimizing a MySQL server database running OLTPBench
    • Optimizing a live K8s deployment
    • Optimizing a live full-stack deployment (K8s + JVM)
  • Akamas Free Trial
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  • Dry-running the optimization study
  • Baselining the system
  • Backuping the original configuration

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  1. Using Akamas
  2. Running optimization studies

Before running optimization studies

The following provides some best practices that can be adopted before launching optimization studies, in particular for offline optimization studies.

Dry-running the optimization study

It is recommended to execute a dry-run of the study to verify that the workflow works as expected and in particular that the telemetry and configuration management steps are correctly executed.

Verify that workflow actually works

It is important to verify that all the steps of the workflow complete successfully and produce the expected results.

Verify that parameters are applied and effective

When approaching the optimization of new applications or technologies, it is important to make sure all the parameters that are being set are actually applied and used by the system.

Depending on the specific technology at hand, the following issues can be found:

  • parameters were set but they are not applied - for example parameters were set in the wrong configuration file or the path is not correct;

  • some automatic (corrective) mechanisms are in place that overrides the values applied for the parameters.

Therefore, it is important to always verify the actual values of the parameters once the system is up & running with a new configuration, and make sure they match the values applied by Akamas. This is typically done by leveraging:

  • monitoring tools, when the parameters are available as metrics or properties of the system;

  • native administration tools, which are typically available for introspection or troubleshooting activities (e.g. jcmd for the JVM).

Verify that load testing works

It is important to verify that the integration with load testing tools actually executes the intended load test scenarios.

Verify that telemetry collects all the relevant metrics

It is important to make sure that the integration with telemetry providers works correctly and that all the relevant metrics of the system are correctly collected.

Data-gathering from the telemetry data sources is launched at the end of the workflow tasks. The status of the telemetry process can be inspected in the Progress tab, where it is also possible to inspect the telemetry logs in case of failures.

Please notice that the telemetry process fails if the key metrics of the study cannot be gathered. This includes metrics defined in the goal function or constraints.

Baselining the system

Before running the optimization study, it is important to make sure the system and the environment where the optimization is running provide stable and reproducible performance.

Make sure the system performance is stable

In order to ensure a successful optimization, it is important to make sure that the target system displays stable and predictable performance and does not suffer from random variations.

To make sure this is the case, it is recommended to create a study that only runs a single baseline experiment. In order to assess the performance of the system, Akamas trials can be used to execute the same experiments (hence, the same configuration) multiple times (e.g. three times). Once the experiment is completed, the resulting performance metrics can be analyzed to assess the stability. The analysis can either be done by leveraging aggregate metrics in the Analysis tab, or to a deeper level on the actual time series by accessing the Metrics tab from the Akamas UI.

Ideally, no significant performance variation should be observed in the different trials, for the key system performance metrics. Otherwise, it is strongly recommended to identify the root cause before proceeding with the actual optimization activity.

Backuping the original configuration

Before launching the optimization it might be a good idea to take note of (or backup) the original configuration. This is very important in the case of Linux OS parameters optimization.

Last updated 1 year ago

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Inspecting the data gathering logs from the Akamas UI