一句话：提出 Erms 系统，通过延迟画像、资源伸缩与调度策略，在共享微服务中保证 SLA 并提升资源效率。
数据中心常按实际负载动态供给资源以提高利用率，但微服务中不同组件对端到端性能影响差异大，且同一微服务会被多个负载模式和 SLA 不同的在线服务共享，资源管理困难。本文提出 Erms，将微服务延迟建模为负载、资源使用和干扰的分段线性函数，并据此构建资源伸缩模型，为复杂依赖的微服务确定延迟目标，同时设计共享微服务调度策略。在微服务基准和 trace 驱动仿真中，Erms 相比现有方法将 SLA 违约概率降低 5 倍，资源使用降低 1.6 倍。
A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements. We present an efficient resource management system, namely Erms, for guaranteeing SLAs in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to further enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.
@inproceedings{LuoXYXZHY023,
title = {Erms: Efficient Resource Management for Shared Microservices with SLA Guarantees},
author = {Shutian Luo and Huanle Xu and Kejiang Ye and Guoyao Xu and Liping Zhang and Jian He and Guodong Yang and Chengzhong Xu},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3567955.3567964}
}