Exploring Efficient Microservice Level Parallelism

Xinkai Wang, Chao Li, Lu Zhang, Xiaofeng Hou, Quan Chen, Minyi Guo

Abstract: The microservice architecture has recently become a driving trend in the cloud by disaggregating a monolithic application into many scenario-oriented service blocks (microservices). The decomposition process results in a highly dynamic execution scenario, in which various chained microservices contend for computing resources in different ways. While parallelism has been exploited at both the instruction/thread level and the task/request level, very limited work has been done with the grain-size of a microservice. Current parallel processing solutions are sub-optimal as they neither capture the unique characteristics of microservices nor consider the uncertainty arises in the microservice environment. In this work we introduce microservice level parallelism (MLP), a technique that aims to precisely coalesce and align parallel microservice chains for better system performance and resource utilization. We identify major issues that prevent servers from effectively exploiting MLP and we define metrics that can guide MLP optimization. We propose v-MLP, a volatility-aware MLP that is able to adapt to a highly heterogeneous and dynamic microservice environment. We show that v-MLP can reduce tail latency by up to 50% and improve resource utilization by up to 15 % under various scenarios.