Energy-Aware Scheduling for Precedence-Constrained Parallel Virtual Machines in Virtualized Data Centers

Abstract

Large scale Internet services are expected to only increase in complexity and popularity. Their energy consumption is also a major concern in data centers. Smart scheduling of their sub-services on data center Physical Machines (PM) can effectively improve their energy as well as performance. Since today servers are not energy-proportional yet, a major and traditionally neglected source of inefficiency in them is the utilization level of PMs. We present two scheduling algorithms for precedence-constrained parallel Virtual Machines (VM) in a virtualized data center where each VM represents a sub-service of the Internet-scale service. Our algorithms use virtualization technology to increase utilization of the PMs, and hence reduce total number of active PMs, to improve energy with minimal effect on makespan. Both proposed algorithms have a polynomial time complexity which make them suitable options for scheduling of large services. Simulation results using real-world services demonstrate that the algorithms are capable of increasing utilization level of PMs on average by 52 % and improving energy consumption by 18 % while the makespan of services is degraded less than 2 %.