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Enabling Practical Cloud Performance Debugging with Unsupervised Learning

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Titel: Enabling Practical Cloud Performance Debugging with Unsupervised Learning
Autoren: Yu Gan, Mingyu Liang, Sundar Dev, David Lo, Christina Delimitrou
Quelle: ACM SIGOPS Operating Systems Review. 56:34-41
Verlagsinformationen: Association for Computing Machinery (ACM), 2022.
Publikationsjahr: 2022
Schlagwörter: 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences
Beschreibung: Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite their benefits, microservices are prone to cascading performance issues, and can lead to prolonged periods of degraded performance. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices that is both accurate and practical. We show that Sage correctly identifies the root causes of performance issues across a diverse set of microservices and takes action to address them, leading to more predictable, performant, and efficient cloud systems.
Publikationsart: Article
Sprache: English
ISSN: 0163-5980
DOI: 10.1145/3544497.3544503
Rights: URL: https://www.acm.org/publications/policies/copyright_policy#Background
Dokumentencode: edsair.doi...........e41d725733c35c26e2b8306aedefe0e4
Datenbank: OpenAIRE
Beschreibung
Abstract:Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite their benefits, microservices are prone to cascading performance issues, and can lead to prolonged periods of degraded performance. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices that is both accurate and practical. We show that Sage correctly identifies the root causes of performance issues across a diverse set of microservices and takes action to address them, leading to more predictable, performant, and efficient cloud systems.
ISSN:01635980
DOI:10.1145/3544497.3544503