An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

Cloud computingprovides computingresources like softwareandhardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since theproblemof taskschedulingis oneof the NondeterministicPolynomial-time (NP)-hard problems, meta-heuristicalgorithms have been widely employedforsolvingtask schedulingproblems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposedan energy-aware andcost-efficient SOA-basedTaskScheduling(SOATS) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan,cost,waitingtime,andloadbalancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we comparedthe proposedalgorithmwith well-known schedulingmethods: Cost-basedJob Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy-GA (FUGE). In the heavilyloadedenvironment, the SOATSalgorithmimprovedenergy consumption and cost saving by 10 and 25%, respectively. © 2022 Materials and Energy Research Center. All rights reserved.