一句话：提出MicroRes，首个通过退化传播索引实现微服务通用韧性剖析的框架。
微服务的韧性对云厂商至关重要，但现有实践依赖针对特定系统人工配置的规则，在大规模高动态场景下劳动密集且缺乏灵活性。本文提出MicroRes，首个基于退化传播索引的通用微服务韧性剖析框架。其洞见是：有韧性的部署能阻止退化从系统性能指标传播到用户感知指标。MicroRes先注入故障并收集监控指标，再按各指标对整体服务退化的贡献排序，并依据退化从系统性能指标向用户感知指标传播的程度构造韧性指数——传播越高，韧性越低。作者在两个开源和一个工业微服务系统上评估，实验表明其能高效、有效地剖析韧性，并展示生产应用。
Microservice resilience, the ability of microservices to recover from failures and continue providing reliable and responsive services, is crucial for cloud vendors. However, the current practice relies on manually configured rules specific to a certain microservice system, resulting in labor-intensity and flexibility issues, given the large scale and high dynamics of microservices. A more labor-efficient and versatile solution is desired. Our insight is that resilient deployment can effectively prevent the dissemination of degradation from system performance metrics to user-aware metrics, and the latter directly affects service quality. In other words, failures in a non-resilient deployment can impact both types of metrics, leading to user dissatisfaction. With this in mind, we propose MicroRes, the first versatile resilience profiling framework for microservices via degradation dissemination indexing. MicroRes first injects failures into microservices and collects available monitoring metrics. Then, it ranks the metrics according to their contributions to the overall service degradation. It produces a resilience index by how much the degradation is disseminated from system performance metrics to user-aware metrics. Higher degradation dissemination indicates lower resilience. We evaluate MicroRes on two open-source and one industrial microservice system. The experiments show MicroRes' efficient and effective resilience profiling of microservices. We also showcase MicroRes' practical usage in production.
DOI 原文 ·
@inproceedings{paperbot8485,
title = {MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing},
author = {Tianyi Yang and Cheryl Lee and Jiacheng Shen and Yuxin Su and Cong Feng and Yongqiang Yang and Michael R. Lyu},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3652131}
}