A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

The virtual machine (VM) allocation problem is one of the main issues in the cloud data centers. This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement in the cloud data center called JTSVMP. The JTSVMP problem composed of two parts, namely task scheduling and VM placement, is carried out by using metaheuristic optimization algorithms (MOAs). The proposed method aims to schedule task into the VM which has the least execution cost within deadline constraint and then place the selected VM on most utilized physical host (PH) within capacity constraint. To evaluate the performance of the proposed method, we compare the performance of task scheduling algorithms only with others that integrate both task scheduling and VM placement using MOAs, namely the basic glowworm swarm optimization (GSO), moth-flame glowworm swarm optimization (MFGSO) and genetic algorithm (GA). Simulation results show that optimizing joint task scheduling and VM placement algorithm leads to better overall results in terms of minimizing execution cost, makespan and degree of imbalance and maximizing PHs resource utilization