An approach for cloud resource scheduling based on Parallel Genetic Algorithm

Resource scheduling is a key process for clouds such as Infrastructure as a Service cloud. To make the most efficient use of the resources, we propose an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling problems. We investigate the possibility to place the Virtual Machines in a flexible way to improve the speed of finding the best allocation on the premise of permitting the maximum utilization of resources. Mathematically, we consider the scheduling problem come down to an Unbalance Assignment Problem. Our scheduling policy achieved by Parallel Genetic Algorithm which is much faster than traditional Genetic Algorithm. The experiments show that our method improved both the speed of resources allocation and the utilization of system resource.