Quick Guides
  • Free Trial options
  • 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
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. Quick Guides: Akamas in a sandbox
  2. [AIAS-03] Guide: Create a study to optimize K8s microservices costs using the Akamas CLI

[AIAS-03] Explore the results

Previous[AIAS-03] Create the StudyNextQuick Guides: Akamas in a box

Last updated 10 months ago

Was this helpful?

Let's now take a look at the results and benefits Akamas achieved in this optimization study.

First of all, the best configuration was quickly identified, providing a cost-efficiency increase of 17%, without affecting the response time.

Let's look at the best configuration in the Summary tab. Here you can see the right amount Akamas AI found for all CPU and memory requests & limits, considering the goal of maximizing cost efficiency and matching the application performance and reliability constraints.

It’s interesting to notice the best configuration Akamas found for every single microservice:

  • For some microservices (e.g., frontend), both the CPU and memory resources were increased.

  • For others (e.g., paymentservice), the memory was decreased while the CPU was slightly increased.

  • For some others (e.g., productcatalogservice), only the memory was decreased.

Let's navigate the Insights section, which provides details of the best experiment for each of the selected KPIs.

The best experiments according to the selected KPIs are automatically tagged and listed in the table. Interestingly, experiment 34 reached the best efficiency, while experiment 53 achieved the best throughput and a significant decrease in the application response time. Also, notice that a couple of identified configurations improved the application response time even more (up to 87%)!

The experiments can be plotted with the histogram icon to better analyze the impact of the selected configurations.

This optimization study shows how it is possible to tune a Kubernetes application made by several microservices, which represents a complex challenge, typically requiring days or weeks of time and effort even for expert performance engineers, developers, or SRE. With Akamas, the optimization study only took about 4 hours to automatically identify the optimal configuration for each Kubernetes microservice.

Congratulations! You have completed your first study to optimize a Kubernetes application! As a next step, take a look at all the other available in our free trial sandbox. Have you already completed all of the free trial guides? Get in touch with us and share your feedback!

guides