ShapleyIQ: Influence Quantification by Shapley Values for Performance Debugging of Microservices
Ye Li, Jian Rong Tan, Bin Wu, Xiao He, Feifei Li · 2023
Years of experience in operating large-scale microservice systems strengthens our belief that their individual components, with inevitable anomalies, still demand a quantification of the influences on the end-to-end performance indicators. On a causal graph that represents the complex dependencies between the system components, the scatteredly detected anomalies, even when they look similar, could have different implications with contrastive remedial actions. To this end, we design ShapleyIQ, an online monitoring and diagnosis service that can effectively improve the system stability. It is guided by rigorous analysis on Shapley values for a causal graph. Notably, a new property on splitting invariance addresses the challenging exponential computation complexity problem of generic Shapley values by decomposition.