The Hybrid Optimization Algorithm for Load Balancing in Cloud 
Pooja Arora1, Anurag Dixit2
1Pooja Arora, Department of IT, BCIIT, Delhi, India.
2Anurag Dixit, Department of SCSE, Galgotias University, Delhi, India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 67-71 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10170785S319/19©BEIESP | DOI: 10.35940/ijeat.E1017.0785S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Cloud Computing, Load Balancing, Elephant Herding Optimization, Grey Wolf Optimizer, Reallocation, Pitch Factors.
Scope of the Article: Cloud Computing and Networking