Morpheus: towards automated SLOs for enterprise clusters

Abstract

Modern resource management frameworks for large-scale analytics leave unresolved the problematic tension between high cluster utilization and job's performance predictability--respectively coveted by operators and users. We address this in Morpheus, a new system that: 1) codifies implicit user expectations as explicit Service Level Objectives (SLOs), inferred from historical data, 2) enforces SLOs using novel scheduling techniques that isolate jobs from sharing-induced performance variability, and 3) mitigates inherent performance variance (e.g., due to failures) by means of dynamic reprovisioning of jobs. We validate these ideas against production traces from a 50k node cluster, and show that Morpheus can lower the number of deadline violations by 5× to 13×, while retaining cluster-utilization, and lowering cluster footprint by 14% to 28%. We demonstrate the scalability and practicality of our implementation by deploying Morpheus on a 2700-node cluster and running it against production-derived workloads.