Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers

Despite the dramatic improvements achieved in building energy-efficient electronic devices, the amount of electricity consumed worldwide to power the global information technology infrastructure has grown tremendously in the past decade. In this paper, we propose algorithms to reduce energy consumption by data centers by considering the placement of virtual machines onto the servers in the data center intelligently. We formulate this problem as an integer programming problem, prove it is NP-hard, then explore two greedy approximation algorithms, minimum energy virtual machine (VM) scheduling algorithm (MinES) and minimum communication virtual machine scheduling algorithm (MinCS), to reduce the energy while satisfying the tenants' service level agreements. We examine the performance of these two algorithms in both small and large clusters using real data traces and synthetic workloads, and compare them to other alternatives. Our results demonstrate that MinES and MinCS yield scheduling that are within 4.3 to 6.1 percent energy consumption of the optimal solution while being computationally efficient.