Analysis of Dynamically Switching Energy-Aware Scheduling Policies for Varying Workloads

Abstract

Over the past decade, the compute resource requirements of applications has continued to grow. This leads to an increase in the required infrastructure that poses additional challenges to mitigating energy consumption and peak power draws. Apache Mesos has been a dominant player in the cluster management space, acting as a middleware between frameworks and the underlying infrastructure. Due to its high availability, fault-tolerance, and scalability, Mesos is used in the industry to run massive scale workloads. Datacenters typically handle continuous dynamically varying workloads, potentially leading to high peak power and energy consumption, in-turn leading to high cost of operation. Given a set of scheduling policies that have been proven to be beneficial in lowering peak power and/or energy consumption, we propose a mechanism of switching between these policies in order to adapt to the dynamic variation in the workloads and the changes in the state of the cluster. Our experiments show that adapting to the variation in the workload can lead to a 9.2% reduction in max peak power consumption, 6.9% in total energy consumption, and a 6.4% reduction in makespan when compared to using a single scheduling policy.