Multi-Objective of Load Balancing in Cloud Computing using Cuckoo Search Optimization based Simulation Annealing
Keywords:
Cloud computing, Data center, Improved cuckoo search optimization, Load balancing, Simulation annealing, Virtual machineAbstract
Load balancing (LB) in Cloud computing (CC) is the most challenging and helpful research for distributing tasks between Virtual Machines (VMs) at Data Centers (DC). In the CC environment, the tasks are allocated between VMs and have various frame lengths, initial times as well and execution times. The LB is one of the most significant problems in CC and solving these problems leads to reducing the response time, energy consumption, and cost. In this study, a hybrid method of Cuckoo Search Optimization (CSO) and Simulation Annealing (SA) algorithm called CSSA is proposed for efficiently balancing the load in VMs. This approach updates the search space of SA by using the CSO approach by considering the multi-objectives of cost, Resource Utilization (RU), response time and Degree of Imbalance (DoI). The experimental outcomes show that the proposed CSSA delivers the performance metrics such as makespan, Degree of Imbalance, Resource utilization and Response time and achieved values of about 150.09, 36.62, 0.45 and 2467 by using the no. of tasks of 2000, which ensures better results compared with the existing methods named BSASSO, QMPSO and MMHHO.
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