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Multi‐objective approach of energy efficient workflow scheduling in cloud environments
Author(s) -
Rehman Attiqa,
Hussain Syed S.,
Rehman Zia,
Zia Seemal,
Shamshirband Shahaboddin
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4949
Subject(s) - cloud computing , computer science , job shop scheduling , scheduling (production processes) , workflow , particle swarm optimization , distributed computing , mathematical optimization , energy consumption , efficient energy use , algorithm , engineering , database , computer network , mathematics , operating system , routing (electronic design automation) , electrical engineering
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.