Demeter: QoS-Aware CPU Scheduling to Reduce Power Consumption of Multiple Black-Box Workloads

Abstract

Energy consumption in cloud data centers has become an increasingly important contributor to greenhouse gas emissions and operation costs. To reduce energy-related costs and improve environmental sustainability, most modern data centers consolidate Virtual Machine (VM) workloads belonging to different application classes, some being latency-critical (LC) and others being more tolerant to performance changes, known as best-effort (BE). However, in public cloud scenarios, the real classes of applications are often opaque to data center operators. The heterogeneous applications from different cloud tenants are usually consolidated onto the same hosts to improve energy efficiency, but it is not trivial to guarantee decent performance isolation among colocated workloads. We tackle the above challenges by introducing Demeter, a QoS-aware power management controller for heterogeneous black-box workloads in public clouds. Demeter is designed to work without offline profiling or prior knowledge about black-box workloads. Through the correlation analysis between network throughput and CPU resource utilization, Demeter automatically classifies black-box workloads as either LC or BE. By provisioning differentiated CPU management strategies (including dynamic core allocation and frequency scaling) to LC and BE workloads, Demeter achieves considerable power savings together with a minimum impact on the performance of all workloads. We discuss the design and implementation of Demeter in this work, and conduct extensive experimental evaluations to reveal its effectiveness. Our results show that Demeter not only meets the performance demand of all workloads, but also responds quickly to dynamic load changes in our cloud environment. In addition, Demeter saves an average of 10.6% power consumption than state of the art mechanisms.