FIRM: An intelligent Fine-Grained Resource Management Framework for SLO-Ooritented Microservices

Posted 2021-09-09 银灯玉箫

{FIRM: An intelligent Fine-Grained Resource Management Framework for SLO-Ooritented Microservices}

{2020}, {Haoran Qiu}, {OSDI}

Summary

写完笔记之后最后填，概述文章的内容，以后查阅笔记的时候先看这一段。注：写文章summary切记需要通过自己的思考，用自己的语言描述。忌讳直接Ctrl + c原文。

Research Objective(s)

User-facing latency-sensitive web services including numerous distributed, intercommunicating microservices that promise to simpify software development and operation. However, mulitplexing of computing resources across microservices is still challenging in production because contention for shared resoures 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 © take actions to mitigate SLO violations via dynamic reprovisioning. Experiments across four microservice benchmarks demonstrate that FIRM reduces SLO violations by up tp 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.

Background / Problem Statement

These microservices must handle diverse load characteristics while
efficiently multiplexing shared resources in order to maintain SLOs like end-to-end latnecy.

前人工作未能解决的问题
Unfortunately, these approaches suffer from two main problems. First, they fail to efficiently multiplex resources, such as caches, memory, I/O channels and network links, at fine granularity, and thus may not reduce SLO violations.
Second, significant human-effort and training are needed to build high-fidelity performance models of large-scale microservice deployments that capture low-level resource contention.

Method(s)

1. Support vector machine (SVM) driven detectiion and localization of SLO violations to individual microservice instances. FIRM first identifies the “critical paths”, and then uses per-critical-path and per-microservice-instance performance variability metrics(e.g. sojourn time[1]) to output a binary decision on whether or not a microservice instance is responsible for SLO violations.
2. Reinforement learning(RL) driven mitigation of SLO violations that reduces contention on shared resources. FIRM then user resource utilization, workload characteristics, and performance metrics to make dynamic reprovisioning decisions, which inlcudes (a) increasing or reducing the partition portion of limit for a resource type, (b) scaling up/down, i.e., adding or reducing the amount of resources attached to a container, and © scaling out/in, i.e,. scaling the number of replicas for services. By continuing to learn mitigation policies through reinforcement, FIRM can optimize for dynamic workload-specific characteristics.

Evaluation

作者如何评估自己的方法？实验的setup是什么样的？感兴趣实验数据和结果有哪些？有没有问题或者可以借鉴的地方？

Conclusion

作者给出了哪些结论？哪些是strong conclusions, 哪些又是weak的conclusions（即作者并没有通过实验提供evidence，只在discussion中提到；或实验的数据并没有给出充分的evidence）?

Notes

(optional) 不在以上列表中，但需要特别记录的笔记。

References

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