Publication
CLOUD 2022
Conference paper

MicroLens: A Performance Analysis Framework for Microservices Using Hidden Metrics With BPF

Abstract

Determining the root cause of performance regres-sion for microservices is challenging. The topological cascadingperformance implications among microservices hide the sourceof the problem. Additionally, the lack of knowledge about appli-cation phases can potentially lead to false-positive critical servicedetection. Service resource utilization is an imperfect proxy forapplication performance, potentially leading to false positives.Therefore, in this work, we propose a new performance testingframework that leverages hidden Berkeley Packet Filter (BPF)kernel metrics to locate root causes of performance regression.The framework applies a systematic multi-level approach toanalyze microservice performance without intrusive code instru-mentation. First, the framework constructs an attributed graphwith microservice requests, scores the services to identify thecritical paths, and ranks the low-level metrics to highlight the rootcause of performance regression. Through judiciously designedexperiments, we evaluated the metric collection overhead, show-ing less than 18% more latency when the application is runningacross hosts and 9% within the same host. In addition, dependingon the application, no overhead is experienced, while the state-of-the-art approach presented up to 1060% more latency. Themicroservice benchmark evaluation shows that MicroLens cansuccessfully identify the set of root causes and that the causesvary when the application is running in different infrastructures.

Date

11 Jul 2022

Publication

CLOUD 2022

Authors

Topics

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