Multi‐objective approach of energy efficient workflow scheduling in cloud environments. (30th August 2018)

• Record Type: Journal Article
• Authors: Rehman, Attiqa; Hussain, Syed S.; ur Rehman, Zia; Zia, Seemal; Shamshirband, Shahaboddin
• Other Names: Shojafar Mohammad guestEditor.; Pooranian Zahra guestEditor.; Sookhak Mehdi guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 8(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-30
• Subjects: cloud resources -- dynamic voltage frequency scaling -- genetic algorithm -- makespan -- multi‐objective optimization; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4949
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Scheduling the tasks of a workflow to the cloud resources is a well‐known N‐P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi‐Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources. We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi‐objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.