Enabling Practical Cloud Performance Debugging with Unsupervised Learning
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| Název: | Enabling Practical Cloud Performance Debugging with Unsupervised Learning |
|---|---|
| Autoři: | Yu Gan, Mingyu Liang, Sundar Dev, David Lo, Christina Delimitrou |
| Zdroj: | ACM SIGOPS Operating Systems Review. 56:34-41 |
| Informace o vydavateli: | Association for Computing Machinery (ACM), 2022. |
| Rok vydání: | 2022 |
| Témata: | 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences |
| Popis: | 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. |
| Druh dokumentu: | Article |
| Jazyk: | English |
| ISSN: | 0163-5980 |
| DOI: | 10.1145/3544497.3544503 |
| Rights: | URL: https://www.acm.org/publications/policies/copyright_policy#Background |
| Přístupové číslo: | edsair.doi...........e41d725733c35c26e2b8306aedefe0e4 |
| Databáze: | OpenAIRE |
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