The release of Akamas 1.12 is focused on improved ease of use and integrations. More precisely, this release provides several important enhancements, including:
Optimization Packs are the other key ingredient together with the AI-powered optimization engine in Akamas secret sauce for effectively empowering users in their optimization activities. Typically, in almost every product release, new or upgrades to existing Optimization Packs are being released.
In this release, the Optimization Packs for Oracle and Postgres have been improved by adding support for the latest versions of these popular databases. The following Akamas UI screenshots show the (partial) list of available Optimization Packs and a detail of the Optimization Pack for Oracle 19c.
Akamas offered native integrations with several performance testing tools such as NeoLoad, JMeter, and LoadRunner Professional to let performance teams embrace the new AI-driven performance optimization approach without leaving their existing solutions.
In this release, a new integration with LoadRunner Enterprise has been added to support performance teams that are leveraging this product of the LoadRunner family for centralizing all their load testing activities. As for LoadRunner Professional, this integration with LoadRunner Enterprise provides a new workflow operator to trigger the execution of performance tests and a new Telemetry provider which allows automatically importing performance test metrics such as transactions throughput and response time. This integration has been developed by leveraging standard LoadRunner Enterprise APIs, based on a design validated by Micro Focus Product Management team.
The integrations with LoadRunner Enterprise and LoadRunner Professional now enable performance teams to reuse their existing performance tests based on Micro Focus technology for their optimization activities.
A common type of performance test engineers do is capacity testing, which aims at determining the maximum number of users or throughput the application can sustain before performance requirements (possibly corresponding to Service Level Objectives, or SLOs) are no longer met. In such situations, performance engineers spend a lot of time analyzing load test results, for example, to identify the maximum throughput while some SLOs are met like maximum response time or error rates.
Akamas already featured automated performance scoring (known as windowing), for example, to automatically discard performance tests warm-up and tear-down periods and compute an accurate experiment score.
In this release, Akamas automated scoring has been enhanced to better support performance constraints. For example, you can create a study with an optimization goal set to maximize system throughput while the response time is below 100 ms and the error rate < 1%. Akamas analyzes performance test metrics (e.g. from JMeter, NeoLoad, or LoadRunner) and automatically identifies the performance score as the maximum throughput the system reached while your SLOs are not violated (see the following picture from the Akamas UI).
In this latest release, the Akamas UI has been further improved to provide all the information required to keep track of the progress of your optimization studies and ease troubleshooting. In particular, messages and logs now provide more insights on all the aspects of the optimization process from the configuration of optimized parameters, to the telemetry collection of performance metrics.
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