Akamas Docs
3.1.3
3.1.3
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  • Using Akamas
    • General optimization process and methodology
    • Preparing optimization studies
      • Modeling systems
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        • Creating custom optimization packs
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      • Creating telemetry instances
      • Creating automation workflows
        • Creating workflows for offline studies
        • Performing load testing to support optimization activities
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    • 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)
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    • Guidelines for defining optimization studies
      • Optimizing Linux
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  • Integrating Akamas
    • Integrating Telemetry Providers
      • CSV provider
        • Install CSV provider
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        • Install Dynatrace provider
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        • Install Prometheus provider
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  • Akamas Reference
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      • Workspace
    • Construct templates
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        • Goal & Constraints
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      • FileConfigurator Operator
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    • Telemetry metric mapping
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    • Optimization Packs
      • Linux optimization pack
        • Amazon Linux
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      • Java OpenJDK optimization pack
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        • IBM J9 VM 6
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    • Command Line commands
      • Administration commands
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    • 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)
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  1. Using Akamas
  2. Running optimization studies

Analyzing results of offline optimization studies

Last updated 2 years ago

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Since an offline optimization study lasts for at most the number of configured experiments and typically runs in a test or pre-production environment, results could be safely either analyzed after the study has completely finished.

However, it is a good practice to analyze partial results while the study is still running as this may provide useful insights about both the system being optimized (e.g. understanding of the system dynamics and sub-optimal configurations that could be immediately applied) and about the optimization study itself (e.g. how to re-design a workflow or change constraints), early-on.

The Akamas UI displays the results of an offline optimization study in different visual areas:

  • the Best Configuration section provides the optimal configuration identified by Akamas, as a list of recommended values for the optimization parameters compared to the baseline and ranked according to their relevance;

  • the Progress tab see the following figures) displays the progression of the study with respect to the study steps, the status of each experiment (and trial), its associated score, and the parameter values of the corresponding configurations; this area is mostly used for study monitoring (e.g. identifying failing workflows) and troubleshooting purposes;

  • the Analysis tab (see the following figures) displays how the baseline and experiments score with respect to the optimization goal, and the values of metrics and parameters for the corresponding configurations; this area supports the analysis of the different configurations;

  • the Metrics tab (see the following figure) displays the behavior of the metrics for all executed experiments (and trials); this area supports both study validation activities and deeper analysis of the system behavior;

the Insights section (see the following figure) displays any suboptimal configurations that have been identified for the study KPIs, and also allows making comparisons among them and the best configuration - the page describes in further detail the Insight section and the insights tags displayed in other areas of the Akamas UI.

Optimization Insights
Best Configuration section
Progress tab showing the study steps and experiments
Progress tab showing the configuration associted to an experiment as compared to the best and baseline
Higher part of the Analysis tab showing scored experiments over time
Lower part of the Analysis tab showing values for each confguration metric and parameter
Analysis tab with selected metrics and relative constraints (toggle on)
Best Score and Insight section of an offline optinmization study