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Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices

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Bibliographic Details
Title: Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices
Authors: Gan, Yu, Liang, Mingyu, Dev, Sundar, Lo, David, Delimitrou, Christina
Publisher Information: 2021-01-01
Document Type: Electronic Resource
Abstract: Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud service's QoS. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.
Index Terms: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance, text
URL: http://arxiv.org/abs/2101.00267
Availability: Open access content. Open access content
Other Numbers: COO oai:arXiv.org:2101.00267
1269521132
Contributing Source: CORNELL UNIV
From OAIster®, provided by the OCLC Cooperative.
Accession Number: edsoai.on1269521132
Database: OAIster
Description
Abstract:Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud service's QoS. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.