Energy-Efficient Hybrid Firefly–Crow Optimization Algorithm for VM Consolidation

Abstract

Virtual machine consolidation (VMC) is a successful approach to enhance resource utilization and reduce energy consumption by minimizing the number of active physical machines in a cloud data center. It can be implemented in a centralized or a distributed fashion. In this paper, an efficient multi-objective-based VM consolidation using hybrid firefly-crow optimization algorithm (HFCOA) is proposed. The proposed HFCOA is a novel approach developed by combining firefly optimization algorithm (FA) with crow search optimization algorithm (CSA). A new multi-objective fitness function is derived based on energy consumption, migration cost, and memory utilization. To analyze the performance of the algorithm, the simulation is carried out in the ClousdSim simulator. The proposed HFCOA is compared with FA and CSA in the same simulation environment. Experimental results show that the proposed hybrid algorithm significantly outperforms the original firefly and crow search algorithms.