![]() Īs a matter of fact, tuning configuration parameters can deeply affect performance of the application. In a world where the ability to deliver new digital services and updated capabilities at business speed is critical, the topic of performance optimization is even more important today than ever. In this chapter, we describe some real-world use cases where the joint use of Akamas and Tricentis NeoLoad testing capabilities help solve this optimization problem, by making reference to a sample containerized Java application. This approach enables human experts to understand the dynamics of even the most complex systems, make decisions about which configuration to best apply, and ensure that systems are continuously kept optimized in a closed-loop fashion. The answers to these questions are addressed by Akamas, a new AI-powered performance optimization approach to automate performance testing, identify optimal configurations based on custom-defined goals, and get them applied to the target systems. ![]() How best to optimize the performance of modern applications sitting atop of so many technology layers? How to find the best configuration when there is an exponentially growing number of settings at each layer, with counterintuitive interplay for each specific application - to the point of heavily affecting the overall performance even when vendor defaults or best practices are adopted? Traditional performance tuning approaches are not effective and do not scale anymore, so significant optimization opportunities are left on the table. The world of performance optimization is changing in response to software becoming more agile and technology becoming more complex. However, despite investments made to improve the quality of code and the time spent in load testing and performance tuning, results are often neither adequate nor cost-effective. Any company offering digital services today needs to ensure that the highest level of performance and availability is delivered to end users.
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