An efficient Task Scheduling Algorithm using Modified Whale Optimization Algorithm in Cloud Computing
1N.P.Saravanan, Asst. Prof (SLG), Dept of CSE, Kongu Engineering College, Perundurai, Tamilnadu, India.
2T.Kumaravel, Asst. Prof, Dept of CSE, Kongu Engineering College, Perundurai, Tamilnadu, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2533-2537 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3813129219/2019©BEIESP | DOI: 10.35940/ijeat.B3813.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing brings computing resources such as software and hardware, it serve service to the users through a network. Major concept of cloud computing is to share the marvellous storage section. In cloud computing, the user jobs are prepared and executed with appropriate resources to successfully deliver the services. There are large amount of task allocation techniques that are used to accomplish task planning. In order to improve the task scheduling technique, so we proposed method of efficient task scheduling algorithm. Optimization techniques are solving NP-hard problems is very famous. In this proposed technique, user tasks are stored in the order of queue methods. The priority is designed and allocated suitable resources for the task. New tasks are investigated and kept in the on-demand priority of queue. The output of the on-demand queue is given to the MWOA. It has been proved that this algorithm is capable to eliminate optimization problems and outperform the current algorithms. The method is proposed to the required more number of iterations is reduced. The proposed algorithm is compared with various scheduling algorithms such as, genetic algorithm, ant colony, standard grey wolf optimization and particle swarm optimization. The outcomes of tests indicate the better efficiency of the MWOA in expressions of makespan and energy consumption.
Keywords: In cloud computing, the user jobs are prepared and executed with appropriate resources to successfully deliver the services.