Dynamic prediction scheduling for virtual machine placement via ant colony optimization

Abstract

Virtual machine (VM) scheduling with load balancing in cloud computing aims to allocate VMs to suitable physical machines (PM) and balance the resource usage among all of the PMs. Correct scheduling of cloud hosts is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. In this regard the use of dynamic forecast of resource usage in each PM can improve the VM scheduling problem. This paper combines ant colony optimization (ACO) and VM dynamic forecast scheduling (VM_DFS), called virtual machine dynamic prediction scheduling via ant colony optimization (VMDPS-ACO), to solve the VM scheduling problem. In this algorithm through analysis of historical memory consumption in each PM, future memory consumption forecast of VMs on that PM and the efficient allocation of VMs on the cloud infrastructure is performed. We experimented the proposed algorithm using Matlab. The performance of the proposed algorithm is compared with VM_DFS. VM_DFS algorithm exploits first fit decreasing (FFD) scheme using corresponding types (i.e. queuing the list of VMs increasingly, decreasingly or randomly) to schedule VMs and assign them to suitable PMs. We experimented the proposed algorithm in both homogeneous and heterogeneous mode. The results indicate, VMDPS-ACO produces lower resource wastage than VM_DFS in both homogenous and heterogeneous modes and better load balancing among PMs.