Quantitative Modeling and Evaluation of Software Aging and Rejuvenation in Microservices

Microservices are typically deployed as replica sets to serve high workloads of requests while guaranteeing high availability and reliability. Being designed for continuous longterm operation, replica-based systems suffer from software aging, leading to progressive performance degradation and possibly to failures. This problem is exacerbated by the heterogeneity of the endpoints of a microservice in terms of workload characteristics, software aging effects produced on the replicas, and QoS requirements. Thus, configuration of replica sets is challenging. In this work, we address quantitative modeling and evaluation of software aging and rejuvenation in replica sets. Specifically, we define Generalized Stochastic Petri Nets (GSPNs) that model the workload, service process, and aging effects characterizing each endpoint, as well as software rejuvenation and repair of the replicas. Steady-state analysis of these models enables deriving quantitative measures of interest, including the expected number of requests yielding a replica failure and the expected number of rejected requests. The analysis results show the effectiveness of our approach in determining the convenience of different replica set configurations, i.e., joined configurations, where two endpoints share a set of replicas, versus separate configurations, where each endpoint has exclusive access to a subset of replicas.