An Evaluation of New Meta-heuristics for Virtual Machine Placement in Cloud Data Centers

The present study investigates the effectiveness of several new meta-heuristic (MH) methods in solving virtual machine (VM) to physical machine (PM) placement (VMP) in cloud data centers. More specifically, Coati optimization algorithm (COA) is properly adapted for solving VMP by introducing several operators for the phases of the algorithm. Several emerging and classic meta-heuristics are also included in the evaluations, including genetic algorithm, chemical reaction optimization, Harris hawk optimization (HHO), and electron valley optimizer (EVO). Two main parameters are included in our evaluations, including power consumption and resource wastage. The algorithms are evaluated in terms of their ability to reduce power consumption and resource wastage in VMP, and also in terms of their execution times. A set of evaluations with synthetic VMs are performed. The results indicate that all MHs perform almost similarly, while emerging methods (COA, HHO, EVO) have a marginal benefit.

Keywords:

Cloud Computing , Coati Optimization Algorithm , Cloud Date Center , Virtual Machine