Sinan: ML-based and QoS-aware Resource Management for Cloud Microservices

Abstract

Cloud applications are increasingly shifting to interactive and loosely-coupled microservices. Despite their advantages, microservices complicate resource management, due to inter-tier dependencies. We present Sinan, a cluster manager for interactive microservices that leverages easily-obtainable tracing data instead of empirical decisions, to infer the impact of a resource allocation on on end-to-end performance, and allocate appropriate resources to each tier. In a preliminary evaluation of Sinan with an end-to-end social network built with microservices, we show that Sinan’s data-driven approach, allows the service to always meet its QoS without sacrificing resource efficiency.

Publication
In the Workshop on ML for Computer Architecture and Systems (MLArchSys 2020)
Zhuangzhuang Zhou
Zhuangzhuang Zhou
Ph.D. Student

My research interests include cloud computing, serverless, microservices, ML for systems and computer architecture.