A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

Abstract

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) isnow critical for the provision of efficient resource management in cloud data centers.The VMP process considers the placement of a set of virtual machines onto a setof physical machines, in accordance with a set of criteria. The optimal solution formulti-objective VMP can be determined by using a fitness function that combines theobjectives. This paper proposes a novel model to enhance the performance of the VMPdecision-making process. Placement decisions are made based on a fitness function thatcombines three criteria: placement time, power consumption, and resource wastage.The proposed model aims to satisfy minimum values for the three objectives forplacement onto all available physical machines. To optimize the VMP solution, theproposed fitness function was implemented using three optimization algorithms:particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization(FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was testedexperimentally. The results of the comparative study between the three algorithmsshow that the hybrid algorithm has the strongest performance. Moreover, the proposedalgorithm was tested against the bin packing best fit strategy. The results show that theproposed algorithm outperforms the best fit strategy in total server utilization