The Hybrid Optimization Algorithm for Load Balancing in Cloud

Abstract

The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.

A response coordination design technology for the main loaded structures of the internal combustion engines based on NSEHO

“…28 A number of improved EHO algorithms based on chaos theory, 29 individual updating strategies, 30 Lévy flight, 31 multisearch strategy, 32 data clustering, 33 and adaptive strategies 34 are proposed to enhance the performance of original EHO. About hybrid EHO algorithms, separating operators with balanced control, 35 fuzzy logic controller, 36 constant function, 37 gray wolf optimizer (GWO), 38 and genetic algorithm (GA) 39 are combined with standard EHO to address special optimization problems in different fields. Regarding the variants, a new binary variant of EHO 40 and a multiobjective clustering EHO 41 are researched for solving binary optimization problems and multiobjective optimization problems, respectively.…”