tldr - powered by Generative AI

• The challenge faced by the customer was to optimize their application while keeping on releasing application updates to introduce new business functionalities and align to new regulations.
• The tuning practice in place was manual and took almost two months to tune one single macro service.
• The AI-powered optimization methodology works in five steps: applying new configuration suggested by AI, applying workload to target system, collecting KPIs, analyzing results, and producing new configuration to be tested in the next iteration.
• The methodology allows setting constraints and goals, such as minimizing application cost and ensuring service reliability.
• The presentation provides an anecdote of how the methodology was used to optimize a customer's authentication service on Kubernetes, resulting in a 49% improvement on cost efficiency compared to the baseline configuration.