A Dynamic Resource Allocation Framework Based On Workload Prediction Algorithm For Cloud Computing
J. Aswini1, N. Malarvizhi2, T. Kumanan3

1J. Aswini, Research Scholar, Department of Computer Science and Engineering,, Meenakshi Academy of Higher Education and Research, Chennai (Tamil Nadu), India, and Assistant Professor, Department of Information Technology, Jawahar Engineering College, Chennai (Tamil Nadu), India.
2N. Malarvizhi , Professor & Head, 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.
3T. Kumanan, Professor, Department of Computer Science and Engineering, Meenakshi Academy of Higher Education and Research, Chennai (Tamil Nadu), India.

Manuscript received on 18 February 2019 | Revised Manuscript received on 27 February 2019 | Manuscript published on 28 February 2019 | PP: 272-277 | Volume-8 Issue-3, February 2019 | Retrieval Number: C5820028319/19©BEIESP
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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: The conventional load balancing algorithms feature severe limitations and drawbacks in cloud environments. In order to address these challenges, researchers have proposed prediction algorithms using genetic algorithms and genetic programming. These algorithms aim to simplify task scheduling in cloud platforms characterized by a large volume of users. The proposed scheme meets the requirements for inter-nodes load balancing. Simulations to compare the performance of the proposed scheme and the AGA demonstrated the effectiveness and validity of the proposed method in cloud computing.
Keywords: Cloud Computing, Resource Allocation, Workload Prediction, Cloudsim

Scope of the Article: Cloud Computing