Energy and Cost-Aware Workflow Scheduling in Cloud Computing Data Centers Using a Multi-objective Optimization Algorithm

Abstract

A multi-objective optimization approach is suggested here for scientific workflow task-scheduling problems in cloud computing. More frequently, scientific workflow involves a large number of tasks. It requires more resources to perform all these tasks. Such a large amount of computing power can be supported only by cloud infrastructure. To implement complex science applications, more computing energy is expended, so the use of cloud virtual machines in an energy-saving way is essential. However, even today, it is a difficult challenge to conduct a scientific workflow in an energy-aware cloud platform. The hardness of this problem increases even more with several contradictory goals. Most of the existing research does not consider the essential characteristic of cloud and significant issues, such as energy variation and throughput besides makespan and cost. Therefore, a hybridization of the Antlion Optimization (ALO) algorithm with the Grasshopper Optimization Algorithm (GOA) was proposed and used multi-objectively to solve the scheduling problems. The novelty of the proposed algorithm was enhancing the search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud environment. The purpose was to minimize the makespan, cost of performing tasks, energy consumption, and increase throughput. WorkflowSim simulator was used for implementation, and the results were compared with the SPEA2 algorithm. Experimental results indicate that based on these metrics, a proposed multi-objective optimization algorithm is better than other similar methods.