ScaleReactor: A graceful performance isolation agent with interference detection and investigation for container‐based scale‐out workloads

Abstract

Striking a balance between improved cluster utilization and guaranteed applicationQoS is a long-standing research problem in multi-tenants shared cluster. The typical solution is to detect performance degradation and investigate the root cause to conduct performance isolation. Existing efforts rely on lots of prior knowledge of applications and the assumption of interference-free workload placement is possible.Performance interference is usually mitigated through application-level approaches such as centralized rescheduling, which is usually an hindsight and a waste of resources.In this article, we present ScaleReactor, a graceful runtime agent on a per node basis that decouples the performance isolation from centralized resource management, and migrates the performance interference of scale-out workloads in container-based cluster using a lightweight black-box approach. ScaleReactor analyzes the degree of contention for multi-dimensional resources among co-located workloads to detect the performance degradation without additional prior information, and uses correlation analysis to locate the cause of contention, while isolating resources in a graceful manner to reduce system overhead and the performance degradation of intrusive workloads. Experiments have demonstrated that ScaleReactor effectively reduces the job completion time of scale-out applications in shared clusters, with the maximum value up to 36% and low system overhead against the existing isolation mechanism.