Deadline Constrained based Resource Allocation in Cloud Environment
N. Malarvizhi1, Aswini. J2, T. Kumanan3
1N. Malarvizhi*, Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
2Aswini. J, Research Scholar, Department of Computer Science and Engineering, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India. & Assistant Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, Tamil Nadu, India.
3T. Kumanan, Professor, Department of Computer Science and Engineering, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1486-1491 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1272109119/2019©BEIESP | DOI: 10.35940/ijeat.A1272.109119
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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 faces a challenge of handling huge amounts of data. The users keep on pushing the data without knowing the challenge in increased storage. Task Scheduling deals with allocating the task to a respective resource pool on a demand basis. Approaches have been built that handle requests from users with deadlines on the amount of request that can be handled. It is important to understand that the mechanism is available to handle the deadlines. The experimental results show that the proposed algorithm produces remarkable performance improvement rate on the total execution cost and total transfer time under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides a better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment.
Keywords: Task scheduling, Deadline constraints, Resource Allocation, Cost optimization.