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  • Quick Guides: Akamas in a sandbox
    • [AIAS-01] Guide: Explore an Optimization Study for a Kubernetes microservices application
      • [AIAS-01] Architecture overview
      • [AIAS-01] Explore the Study
      • [AIAS-01] Explore the System
      • [AIAS-01] Explore the Workflow
      • [AIAS-01] Explore the analysis
      • [AIAS-01] Explore the results
    • [AIAS-02] Guide: Create a study to optimize Java performance using the Akamas UI
      • [AIAS-02] Architecture overview
      • [AIAS-02] Create the Study
      • [AIAS-02] Define the optimization goal
      • [AIAS-02] Define the optimization parameters
      • [AIAS-02] Define the performance metrics
      • [AIAS-02] Define the optimization steps
      • [AIAS-02] Explore the results
    • [AIAS-03] Guide: Create a study to optimize K8s microservices costs using the Akamas CLI
      • [AIAS-03] Architecture overview
      • [AIAS-03] Create the system
      • [AIAS-03] Create the Workflow
      • [AIAS-03] Create the Study
      • [AIAS-03] Explore the results
  • Quick Guides: Akamas in a box
    • [AIAB-00] Install Akamas-in-a-box
      • [AIAB-00] Setup your Linux box
      • [AIAB-00] Install Akamas
    • [AIAB-01] Optimize a Java-based application (Renaissance benchmark)
      • [AIAB-01] Architecture overview
      • [AIAB-01] Create the System and its associated components
      • [AIAB-01] Configure the Telemetry
      • [AIAB-01] Create the workflow
      • [AIAB-01] Create and run the study
      • [AIAB-01] Explore the results
    • [AIAB-02] Optimize a Java-based application (Konakart) with JMeter
      • [AIAB-02] Architecture overview
      • [AIAB-02] Create the system and its components
      • [AIAB-02] Automate performance tests
      • [AIAB-02] Create the Telemetry Provider
      • [AIAB-02] Create the workflow
      • [AIAB-02] Create the study
      • [AIAB-02] Explore the results
    • [AIAB-03] Optimize a Java-based application (Konakart) with LRE
      • [AIAB-03] Architecture overview
      • [AIAB-03] Setup LoadRunner Enterprise
      • [AIAB-03] Create the system and its components
      • [AIAB-03] Create the telemetry instances
      • [AIAB-03] Create the workflow
      • [AIAB-03] Create the optimization study
    • [AIAB-04] Optimize a Java-based Kubernetes application (Online Boutique)
      • [AIAB-04] Architecture overview and setup
      • [AIAB-04] Setup Online Boutique
      • [AIAB-04] Setup Akamas
      • [AIAB-04] Create the system and its components
      • [AIAB-04] Create the workflow
      • [AIAB-04] Create the Study
      • [AIAB-04] Explore the results
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  1. Quick Guides: Akamas in a sandbox
  2. [AIAS-01] Guide: Explore an Optimization Study for a Kubernetes microservices application

[AIAS-01] Explore the results

Previous[AIAS-01] Explore the analysisNext[AIAS-02] Guide: Create a study to optimize Java performance using the Akamas UI

Last updated 10 months ago

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The best configuration Akamas found (for the defined optimization goal and constraints) is displayed at the bottom of the Summary tab. In this table, you can see the optimal value Akamas AI found for each parameter defined in the study optimization scope. You can also see the baseline value, which is the original value the parameter had before the optimization.

Moreover, the Insight section highlights other interesting configurations that Akamas found during the optimization process, with respect to the defined KPIs.

These KPIs are automatically selected by Akamas based on the metrics included in the optimization goal and constraints, but can also be customized by clicking on the "KPIs" section of the Summary page.

By selecting the big right arrow from the Insight section, you can visualize all of the configurations of interest for all the selected KPIs.

You can also quickly compare how different configurations score in terms of the KPIs, by clicking the histogram icon on those configurations of interest.

In this case, it is worth noticing that there is a configuration (#12) with a slightly lower cost reduction goal (-48.9% with respect to -49.1% provided by the best configuration) that provides a slight improvement (+1.4%) in terms of transaction throughput with respect to both the baseline and the best configuration.

With the Insights, you can discover the best configurations that are most interesting for you to optimize your application efficiency and performance. This shows how Insights provides support for a better decision-making process on which configuration to apply.

Congratulations! You have finished the exploration of your first Akamas optimization study. Now things get interesting: you can continue your journey by creating and running your first study! Your free trial sandbox is equipped with sample apps that you can use to play with Akamas AI-driven optimization. Follow the guide to optimize the performance of a Java app using the UI, or the guide to optimize the resource efficiency of K8s deployment requests and limits using the CLI.

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