Optimizing Microservice Resource Configuration in Cloud Native Environments
Analysis
This paper addresses a critical, often overlooked, aspect of microservice performance: upfront resource configuration during the Release phase. It highlights the limitations of solely relying on autoscaling and intelligent scheduling, emphasizing the need for initial fine-tuning of CPU and memory allocation. The research provides practical insights into applying offline optimization techniques, comparing different algorithms, and offering guidance on when to use factor screening versus Bayesian optimization. This is valuable because it moves beyond reactive scaling and focuses on proactive optimization for improved performance and resource efficiency.
Key Takeaways
- •Focuses on proactive resource configuration during the Release phase, complementing autoscaling.
- •Evaluates different optimization algorithms for CPU and memory allocation in microservices.
- •Provides guidance on when to use factor screening and Bayesian optimization based on the optimization goal (optimal vs. near-optimal).
- •Uses the TeaStore microservice application for empirical evaluation.
“Upfront factor screening, for reducing the search space, is helpful when the goal is to find the optimal resource configuration with an affordable sampling budget. When the goal is to statistically compare different algorithms, screening must also be applied to make data collection of all data points in the search space feasible. If the goal is to find a near-optimal configuration, however, it is better to run bayesian optimization without screening.”