[AIAS-02] Explore the results

The study will run for 30 experiments or about 1 hour. After running the study, you can explore the results of your AI-driven performance optimization study.

Select your study from the UI Study menu.

The Summary tab displays high-level study information at a glance, including the best score obtained so far, and a summary of the tuned parameters and their values for the optimal configuration.

In this example, Akamas was able to cut the application response time by 40%. A significant result that was achieved by optimally configuring the application runtime, without any code changes!

What are the best JVM settings Akamas found that made the application run so much faster?

Without being told anything about how the application works, Akamas learned the best settings for some interesting JVM parameters:

• the max heap size was slightly changed

• the best garbage collector is Parallel

The Progress tab allows following the experiments and their workflow tasks execution (including logs for troubleshooting).

The Analysis tab shows the experiments' scores over time, plus a detailed table with key parameters and metrics for each experiment.

Properly tuning a modern JVM is a complex challenge, and might require weeks of time and effort even for performance experts. Akamas AI is designed to converge rapidly toward optimal configurations. In this example, Akamas was able to find the optimal JVM configuration after about 16 automated performance experiments:

The Configuration Analysis tab lets you explore the additional insights and benefits of the configurations Akamas explored with respect to other key metrics besides the goal.

Interestingly, another configuration Akamas found was able to cut CPU utilization by 33%, while still improving response time by 17%. So you improved the performance and reduced costs, at the same time.

The Metrics tab allows you to check the metrics that were collected by the telemetry for each experiment.

Congratulations! You have finished your first study! Continue your journey by following the third guide to learn how to optimize the resource efficiency of K8s deployments requests and limits using the CLI.