The Kubernetes performance tuning dilemma: How to solve it with AI.
There is a “dark side” to Kubernetes that makes it difficult to ensure the desired performance and resilience of cloud-native applications, while also keeping their costs under control. Indeed, the combined effect of Kubernetes resource management mechanisms and application runtime heuristics may cause serious performance and resilience risks.
There are also significant potential improvements, both in terms of performance and efficiency, that can be achieved by properly tuning Kubernetes and application runtime (e.g. JVM, Golang) configuration settings.
In this webinar, we illustrate how the Akamas AI-powered optimizations platform addresses these challenges by making it possible to set the optimization goals (e.g. cost reduction) and constraints (e.g. performance SLOs) and get recommendations on how to adjust configuration settings dynamically under varying workloads.
Stefano is obsessed with performance optimization and leads the Akamas vision for Autonomous Performance Optimization powered by Al. With more than 15 years of experience in the performance industry, he has worked on projects for major national and international enterprises.
Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.