Akamas Docs
3.2.1
3.2.1
  • How to use this documentation
  • Getting started with Akamas
    • Introduction to Akamas
    • Licensing
    • Deployment
      • Cloud Hosting
    • Security
    • Maintenance & Support (M&S) Services
      • Customer Support Services
      • Support levels for Customer Support Services
      • Support levels for software versions
      • Support levels with Akamas
  • Installing Akamas
    • Architecture
    • Docker compose installation
      • 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
      • Troubleshoot Docker installation issues
    • Kubernetes installation
      • Prerequisites
        • Cluster Requirements
        • Software Requirements
      • Install Akamas
        • Online Installation
        • Offline Installation
      • Accessing Akamas
      • HTTPS configuration
    • Install the CLI
      • Setup the CLI
      • Initialize the CLI
      • Change CLI configuration
      • Use a proxy server
    • Verify the installation
    • Management container/pod
    • Install the license
    • Manage anonymous data collection
    • Manage Akamas
      • 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 Kubernetes
      • Guidelines for JVM layer (OpenJDK)
      • Guidelines for JVM (OpenJ9)
      • 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 telemetry instances
      • Dynatrace provider
        • Install Dynatrace provider
        • Create Dynatrace telemetry instances
      • Prometheus provider
        • Install Prometheus provider
        • Create Prometheus telemetry instances
        • CloudWatch Exporter
        • OracleDB Exporter
      • Spark History Server provider
        • Install Spark History Server provider
        • Create Spark History Server telemetry instances
      • NeoLoadWeb provider
        • Install NeoLoadWeb telemetry provider
        • Create NeoLoadWeb telemetry instances
      • LoadRunner Professional provider
        • Install LoadRunner Professional provider
        • Create LoadRunner Professional telemetry instances
      • LoadRunner Enterprise provider
        • Install LoadRunner Enterprise provider
        • Create LoadRunner Enterprise telemetry instances
      • AWS provider
        • Install AWS provider
        • Create AWS telemetry 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
        • Optimizer Options
    • 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
    • Release Notes
  • 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 cost of a Kubernetes application while preserving SLOs in production
    • Optimizing a live full-stack deployment (K8s + JVM)
  • Akamas Free Trial
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On this page
  • What's new in 3.2.1
  • Functionalities
  • Usability
  • Documentation
  • Bug fixes

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  1. Akamas Reference

Release Notes

What's new in 3.2.1

We are pleased to unveil Akamas Version 3.2.1, which introduces a host of new functionalities, usability enhancements, and critical bug fixes. As always, we extend our heartfelt thanks to our vibrant community of users for their invaluable feedback and unwavering support, which continues to drive the growth and improvement of Akamas. Let's take a closer look at the exciting features and improvements in this version!

Functionalities

This latest release introduces several exciting functionalities to enhance your optimization experience. Live Optimizations now offer a comprehensive monitoring and troubleshooting feature via the user interface. Gain real-time insights into configuration evaluation progress, access logs, and analyze optimization benefits effortlessly. Additionally, we've added the ability to select an aggregation function for workload metrics, providing users with greater flexibility and meaningful optimization results. Telemetry now allows users to specify default values for metrics when data sources lack samples, ensuring smoother data handling and analysis in such scenarios.

Usability

We are dedicated to improving user experience and we've made several enhancements to streamline your workflow. The login message now informs users about the default workspace being used, ensuring clarity during onboarding. When deleting systems or telemetry instances, Akamas proactively informs users about potential impacts on running studies, empowering informed decision-making. Additionally, users will now receive alerts if a live optimization cannot proceed due to baseline failures, enabling swift action and troubleshooting. Moreover, we've implemented pre-study validation for systems, telemetry, and workflows to reduce errors and ensure a smoother study setup process.

Documentation

To assist users in leveraging Akamas to its fullest potential, we've made improvements to our documentation. The section on proxy setup for the command-line interface (CLI) has been enhanced, providing clearer instructions and troubleshooting tips. Additionally, we've optimized and refined Helm commands for Akamas installation on Kubernetes, simplifying the process and reducing potential roadblocks.

Bug fixes

As part of our commitment to delivering a reliable product, we've addressed critical bugs. Akamas now accurately tracks the best experiment and Key Performance Indicators (KPIs) during study export and import. Metric violation calculations for relative constraint baselines have been improved. Users can now delete a study even if the related workflow has been previously deleted, resolving any conflicts and enabling smoother study management.

Thank you for your continued support and feedback, which have been invaluable in shaping Akamas Version 3.2.1. We strive to provide an exceptional optimization platform, and your trust in Akamas drives us to improve and innovate to meet your optimization goals.

Last updated 1 year ago

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