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In cloud computing (CC) environment, load balancing of tasks remains as an important problem of distributing resources from a data center to make sure that every virtual machine (VM) have balanced load to attain optimum utilization of its abilities. Load balancing in CC environment is considered as a non-polynomial (NP) problem and metaheursitic algorithms can be applied to resolve it. This paper presents a new quasi oppositional dragonfly algorithm for load balancing (QODA-LB) in CC environment to achieve optimal resource scheduling. The proposed QODA-LB algorithm derives an objective function using three variables namely execution time, execution cost, and load. Based on the derived objective function, the QODA-LB algorithm allocates tasks to VM with respect to its capacity. Besides, the QODA-LB algorithm incorporates quasi oppositional based learning (QOBL) concept to improve the convergence rate of classical dragonfly algorithm (DA). Detailed set of experimentations were carried out to ensure the effective performance of the QODA-LB algorithm and the results are examined under several aspects. The simulation outcome has depicted optimal load balancing performance and demonstrated better results compared to state of art methods.
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