A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling

Workflow is composed of some interdependent tasks and workflow scheduling in the cloud environment that refers to sorting the workflow tasks on virtual machines on the cloud platform. We will encounter many sorting modes with an increase in virtual machines and the variety in task size. Reaching an order with the least makespan is an NP-hard problem. The hardness of this problem increases even more with several contradictory goals. Hence, a meta-heuristic algorithm is what required in reaching the optimal response. Thus, the algorithm is a hybridization of the ant lion optimizer (ALO) algorithm with a Sine Cosine Algorithm (SCA) algorithm and used it multi-objectively to solve the problem of scheduling scientific workflows. The novelty of the proposed algorithm was to enhance search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud computing environment. The purpose was to minimize the makespan and cost of performing tasks, to reduce energy consumption to have a green cloud environment, and to increase throughput. WorkflowSim simulator was used for implementation, and the results were compared with the SPEA2 workflow scheduling algorithm. The results show a decrease in the energy consumed and makespan.