Towards Performance Management of Large-Scale Microservices Applications.
2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)（2023）
Abstract
Microservices architecture is a popular choice for developing large-scale online applications. However, managing and debugging the performance of interconnected microservices can be challenging. This thesis develops techniques for performance management in microservices architecture based on optimization theory and machine learning. The techniques focus on solving two critical problems: configuration tuning, and bottleneck detection and mitigation.
MoreTranslated text
Key words
microservices architecture,configuration tuning,bottleneck detection and mitigation,ML for systems
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined