> For the complete documentation index, see [llms.txt](https://docs.akamas.io/quick-guides/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akamas.io/quick-guides/quick-guides-akamas-in-a-box/aiab-04-optimize-a-java-based-kubernetes-application-online-boutique/aiab-04-explore-the-results.md).
# \[AIAB-04] Explore the results
Let's now take a look at the results and benefits Akamas achieved in this optimization study. Mind that you might achieve different results as the actual best configuration may depend on your actual setup (i.e., operating systems, cloud or virtualization platform, and the hardware).
First of all, the best configuration was quickly identified, providing an application efficiency increase of 17%, without affecting the response time.
Let's look at the best configuration from the Summary tab: this configuration specifies the right amount of CPU and memory for each microservice.
It’s interesting to notice that Akamas did adjust the CPU and memory limits of 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. Experiment 34 reached the best efficiency, while experiment 53 achieves the best throughput and a decrease in the response time. Also, notice that a couple of identified configurations improved the application response time even more (up to 87%) while not representing the best configuration.
The experiments can be plotted using the histogram icon, as shown below.
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.
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