Characterizing Bottlenecks in Scheduling Microservices on Serverless Platforms

Abstract

Datacenters are witnessing an increasing trend in adopting microservice-based architecture for application design, which consists of a combination of different microservices. Typically these applications are short-lived and are administered with strict Service Level Objective (SLO) requirements. Traditional virtual machine (VM) based provisioning for such applications not only suffers from long latency when provisioning resources (as VMs tend to take a few minutes to start up), but also places an additional overhead of server management and provisioning on the users. This led to the adoption of serverless functions, where applications are composed as functions and hosted in containers. However, state-of-the-art schedulers employed in serverless platforms tend to look at microservice-based applications similar to conventional monolithic black-box applications. To detect all the inefficiencies, we characterize the end-to-end life cycle of these microservice-based applications in this work. Our findings show that the applications suffer from poor scheduling of microservices due to reactive container provisioning during workload fluctuations, thereby resulting in either in SLO violations or colossal container over-provisioning, in turn leading to poor resource utilization. We also find that there is an ample amount of slack available at each stage of application execution, which can potentially be leveraged to improve the overall application performance.