This guide explains how to navigate an already executed optimization study that is made available as part of the Akamas-in-a-sandbox (AIAS) environment. This is intended as a first step to then learn how to create an optimization study leveraging the Akamas UI and the CLI.

What you will learn

What you will need

The target system for the optimization study described in this guide is a Java-based microservice acting as authentication service for multiple digital applications all running on Kubernetes.

The reference architecture is illustrated by the following diagram. Notice that the load testing (JMeter) and monitoring (Elastic APM) tools, respectively used to load the target system and to collect the KPIs used in the optimization study.


The goal for this optimization study is to reduce the cost of the Kubernetes infrastructure.

Access the Akamas-in-a-sandbox (AIAS) environment and select "Studies" from the left-hand-side menu.

Select the preloaded "Authentication service - minimize K8s cost" study.

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


At first look, the higher part of the Study page shows:

The following two sections Explore the System and Explore the Workflow are optional as this guide is more focused on analyzing the output of an already excecuted study more than illustrating how to set it up in the first place. Following guides Create an Optimization Study using the UI and Create an Optimization Study and supporting artifacts using the CLI will cover that topic.

The lower part of the Study page shows the Summary:

The optimization goal & constraints can be inspected by clicking on "Details" (and expanding the subsections):


From the Study page, you get to the following page by following the System link


As you can see, the System is represented by several components, whose parameters and metrics you can explore:

From the Study page, you get to the following page by following the Workflow link

As you can see, the Workflow includes several steps which you fully explore by following the left and right arrows



These steps are executed at each experiment to set a specific configuration under test, apply the workoad (in this study this was done by leveraging a JMeter integration as illustrated in Architecture Overview), and finally in calculating the cost associated to this configuration.

The tab "Analysis" in the study shows how Akamas has explored the configuration space by identifying better configurations, with each dot corresponding to an experiement.


This chart shows how quickly better configurations were identified after just a few dozen expertiments (you may also want change the toggle to look at absolute timeframes), with the best configuration discovered at experiment #34.

You can use the table below the chart to explore all experiments (and trials) by analyzing the corresponding values for the differnt metrics and parameters.


Moreover, the tab "Metrics" allows you to analyze all the metrics and parameters over time and to compare their behavious under the baseline and any other configuration explored during the study, including the best configuration.


The best configuration with respect to the optimization goal (and constraints) is displayed at the bottom of the Summary tab.


Moreover, the Insight section highlights other configurations of interest with respect to other KPIs.


These KPIs are automatically selected by Akamas based on the metrics included in the optimization goal and constraints, but can also choosen by clicking on the "KPIs" section of the Summary page.


By selecting the big right arrow from the Insight section is possiuble to visualize all the configurations of interest for all the selected KPIs.

the full view all all the is Study

and see how some of them compare with respect to these KPIs, by activating the histogram icon on those configurations of interest.


In this is case, it is worth noticing that there is a configuration (#12) which sub-optimal with respect to the cost reduction goal (-48.9% with respect to -49.1% provided by the best configuration) that provides a sligth improvement (+1.4%) in terms of transaction throughput with respect to both the baseline and the best configuration. Therefore suboptimal configuration this might be worth to be further explored and possibly selected for being applied in place of the best configuration.


This shows how Akamas Insights provide support for a better decision making process on which configuration to apply.

You have finished your first Akamas optimization of a Kubernetes application.

You can continue exploring Akamas' powerful goal-driven optimization capabilities by leveraging other quick guides or by trying to apply Akamas to your Kubernetes environment.

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