Power-aware scheduling of real-time virtual machines in cloud data centers considering fixed processing intervals

In Cloud data centers, Virtual Machines (VMs) as resources for Infrastructure as a service (IaaS) can be dynamically allocated to different customers to meet their application goals. In this paper, from the providers´ point of view of reducing power consumption, we investigate the power-aware scheduling of real-time VMs by considering fixed processing intervals. In the case of all VMs sharing random portions of the total capacity of a Physical Machine (PM), finding the optimal solution of minimizing the total number of PMs is NP complete as proved in many open literature. Hence, we model the problem as a modified interval partitioning problem to provide approximate solutions and propose scheduling schemes to reduce the power consumption. Simulation results show our proposed scheduling schemes incur 8%-40% less power consumptions than existing algorithms.