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

Cloud computing provides computing resources like software and hardware 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 the problem of task scheduling is one of the nondeterministic polynomial-time (np)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. one of the new bio-inspired meta-algorithms is seagull optimization algorithm (soa). in this paper, we proposed an energy-aware and cost-efficient soa-based task scheduling (soats) algorithm. the aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) 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 compared the proposed algorithm with well-known scheduling methods: cost-based job scheduling (cjs), moth search algorithm based differential evolution (msde), and fuzzy -ga (fuge). in the heavily loaded environment, the soats algorithm improved energy consumption and cost saving by 10 and 25%, respectively.