FIRM：面向SLO微服务的智能细粒度资源管理框架
FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices
摘要：
现代面向用户的延迟敏感的Web服务包括许多分布式的、相互通信的微服务，这些微服务有望简化软件开发和操作。然而，跨微服务的计算资源复用在生产中仍然具有挑战性，因为对共享资源的争用可能会导致延迟峰值，从而违反用户请求的服务级别目标（SLO）。本文介绍了FIRM，这是一种智能细粒度资源管理框架，用于跨微服务可预测地共享资源，以提高整体利用率。FIRM利用在线遥测数据和机器学习方法自适应地（a）检测/定位导致SLO违规的微服务，（b）识别争用的低级资源，以及（c）通过动态重新配置采取行动来减轻SLO违规。在四个微服务基准测试中的实验表明，FIRM将SLO违规减少了16倍，同时将整体请求的CPU限制减少了62%。此外，FIRM通过将尾部延迟减少多达11倍来提高性能可预测性。
英文摘要：
Modern user-facing latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing of compute resources across microservices is still challenging in production because contention for shared resources can cause latency spikes that violate the service-level objectives (SLOs) of user requests. This paper presents FIRM, an intelligent fine-grained resource management framework for predictable sharing of resources across microservices to drive up overall utilization. FIRM leverages online telemetry data and machine-learning methods to adaptively (a) detect/localize microservices that cause SLO violations, (b) identify low-level resources in contention, and (c) take actions to mitigate SLO violations via dynamic reprovisioning. Experiments across four microservice benchmarks demonstrate that FIRM reduces SLO violations by up to 16x while reducing the overall requested CPU limit by up to 62%. Moreover, FIRM improves performance predictability by reducing tail latencies by up to 11x.
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。
