Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Abstract

Internet of things (IoT) and cloud computing (CC) becomes widespread in different application domains such as business, e-commerce, healthcare, etc. The recent developments of IoT technology have led to an increase in large amounts of data from various sources. In IoT enabled cloud environment, load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization. The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics. In this view, this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling (C3SOA-LS) technique for IoT enabled cloud environment. The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished. Besides, the presented C3SOA-LS model involves the design of circle chaotic mapping (CCM) with the traditional chameleon swarm optimization (CSO) algorithm for improving the exploration process, shows the novelty of the work. The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan. The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

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“…The algorithm was initially evaluated successfully with 67 benchmark functions, and since then a variety of implementations have been proposed. The CSA has been implemented for telecommunication and electronic device design [74,75] and developed as a computer system supervisor [76] and dataset manager [77]. It was successfully proven that the CSA can also be used as an intelligent tool for robot suspension synthesis [78].…”