Sage: practical and scalable ML-driven performance debugging in microservices
Yu Gan, Mingyu Liang, Sundar Dev, David Lo, Christina Delimitrou
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. Prior work on performance debugging for cloud services either relies on empirical techniques, or uses supervised learning to diagnose the root causes of performance issues, which requires significant application instrumentation, and is difficult to deploy in practice.
Topics & Concepts
MicroservicesComputer scienceDebuggingScalabilityDistributed computingCloud computingQuality of serviceElasticity (physics)Modularity (biology)Software engineeringService-oriented architectureComputer architectureWeb serviceOperating systemProgramming languageComputer networkComposite materialGeneticsMaterials scienceBiologySoftware System Performance and ReliabilityCloud Computing and Resource ManagementIoT and Edge/Fog Computing