Co-locating containerized workload using service mesh telemetry

The cloud-native architecture and container-based technologies are revolutionizing how online services and applications are designed, developed, and managed by offering better elasticity and flexibility to developers and operators. However, the increasing adoption of microservice and serverless designs makes application workload more decomposed and transient at a larger scale. Most existing container orchestration systems still manage application workload based on simple system-level resource usage and policies manually created by operators, leading to ineffective application-agnostic scheduling and extra management burden for operators. In this work, we focus on workload placement for containerized applications and services and argue for the integration of application-level telemetry for profiling application status and co-locating application workload. To avoid extra performance overhead and modifications to existing applications, we propose to use the telemetry collected by service mesh to model the application communication patterns with a graph-based representation. By applying a graph partitioning algorithm, we create co-location groups for application workload that minimize cross-group communication traffic to improve the overall application performance, i.e., response time. Our preliminary experiments with a realistic online e-commerce application show that our solution can reduce the average response time by up to 58% compared to the default Kubernetes scheduler.