[AIAS-01] Explore the results

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 second guide to optimize the performance of a Java app using the UI, or the third guide to optimize the resource efficiency of K8s deployment requests and limits using the CLI.