云微服务的指标关键性识别
Metric Criticality Identification for Cloud Microservices
AI总结：
本文提出 KIMetrix，利用熵与互信息从微服务轻量级指标和追踪中自动识别最小且全面的关键指标集，实验证明其可提升监控覆盖并减轻 SRE 告警负担。
摘要：
基于微服务架构构建的现代云原生应用给系统监控和告警带来了前所未有的挑战。站点可靠性工程师（SRE）面临一项艰巨任务：在众多指标中定义有效的监控策略以确保系统可靠性，而这项工作传统上需要大量人工专业知识。微服务的分布式特性以随机执行模式和复杂的服务间依赖为特征，使得在庞大指标环境中进行梳理的传统人工方法在计算和运维上都难以承受。为应对这一关键挑战，我们提出 KIMetrix，这是一种数据驱动系统，可自动识别最小但全面的指标子集，以帮助 SRE 监控微服务应用。KIMetrix 利用信息论度量，特别是熵和互信息，来量化指标关键性，同时考虑微服务拓扑中固有的随机执行模式。我们的方法仅依赖轻量级指标和链路追踪，无需对非结构化日志进行昂贵处理，也不需要专家定义的训练数据。在先进的真实微服务基准数据集上的实验评估表明，KIMetrix 能够有效识别关键指标子集，这些子集提供全面的系统覆盖，同时显著减轻 SRE 的负担。通过自动化识别告警所需的关键指标，KIMetrix 实现了更可靠的系统监控，既不会让运维人员被误报淹没，也不会遗漏关键系统事件。
英文摘要：
Modern cloud-native applications built on microservice architectures present unprecedented challenges for system monitoring and alerting. Site Reliability Engineers (SREs) face the daunting challenge of defining effective monitoring strategies across multitude of metrics to ensure system reliability, a task that traditionally requires extensive manual expertise. The distributed nature of microservices, characterized by stochastic execution patterns and intricate inter-service dependencies, renders the traditional manual approach of navigating the vast metrics landscape computationally and operationally prohibitive. To address this critical challenge, we propose KIMetrix, a data-driven system that automatically identifies minimal yet comprehensive metric subsets to aid SREs in monitoring microservice applications. KIMetrix leverages information-theoretic measures, specifically entropy and mutual information, to quantify metric criticality while considering the stochastic execution patterns inherent in microservice topologies. Our approach operates solely on lightweight metrics and traces, eliminating the need for expensive processing of unstructured logs, and requires no expert-defined training data. Experimental evaluation on state-of-the-art real-world microservice benchmark datasets demonstrates KIMetrix's effectiveness in identifying critical metric subsets that provide comprehensive system coverage while significantly reducing the burden on SREs. By automating the identification of essential metrics for alerting, KIMetrix enables more reliable system monitoring without overwhelming operators with false positives or missing critical system events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
