[AIAS-03] Explore the results

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.