As organizations transition to post-pandemic business models, software developers are pressured to conduct application testing faster and earlier in the SDLC while delivering high performance and an optimal user experience. However, this is often easier said than done. As Stefano Doni, Co-Founder & CTO of Akamas detailed in our recent webinar on AI-Driven Performance Optimization, traditional performance optimization comes with the following challenges:
- Configuration Explosion – Properly configuring the IT stack requires analyzing thousands of configurations.
- Unpredictable Effects – Effect of change can be counterintuitive, plus default values are not always appropriate.
- Release at Business Speed – Acceleration of releases often makes manual performance optimization unfeasible.
Manual and trial-and-error performance tuning cannot handle the increased complexity found in today’s faster release cycles and user demands. As a result, incorrect configurations may cause poor application performance, low reliability, and resource (cost) inefficiency.
The good news is that done correctly, performance optimization can deliver huge benefits. In our on-demand webinar Cyrus Manouchehrian, Senior Product Manager, Micro Focus LoadRunner Solutions and Akamas’ Stefano Doni discuss how an AI-driven Performance Optimization solution allows performance engineers to automate testing, automatically identify optimal full-stack configurations to enable their companies to deliver unprecedented application performance and cost saving, while also improving operational resiliency.
Cyrus and Stefano also highlight how the integration of Akamas AI-powered autonomous optimization solution with Micro Focus LoadRunner Enterprise has redefined and modernized the performance engineering process by bringing the following benefits.
- A full-stack solution that can optimize system performance by tuning different layers of the infrastructure.
- Enhanced configuration management thanks to Akamas’ patented artificial intelligence algorithms based on reinforcement learning. These ensure that the full set of configurations found is optimized and computed in a short amount of time.
- Custom goals can be set on any metric being gathered from any system under test. Testers can also define a set of constraints to reflect any SLOs defined for the corresponding service.
- Tools that support closed loop automation. Configuration changes and trigger load tests can be executed in a fully autonomous, unsupervised manner. Akamas’ solution is designed to be integrated with CI/CD pipelines, ensuring a performance gate on your codebase.
During the webinar, Cyrus and Stefano also conduct a full demo that shows how the combined solution optimizes a Java-based e-commerce application.
Click here to access the on-demand webinar and get started on your performance engineering modernization!
More information on Micro Focus LoadRunner Enterprise is available here (https://www.microfocus.com/en-us/products/loadrunner-enterprise/overview)