Scheduling to Maximize the Data Transfer Rate for Big Data Applications in Cloud System
D. Sugumaran1, C. R. Bharathi2
1D. Sugumaran, Department of Information Technology, Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Dr. C. R. Bharathi, Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
Manuscript received on 16 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 29 August 2019 | PP: 255-258 | Volume-8 Issue-2S5 July 2019 | Retrieval Number: B10530682S519/2019©BEIESP | DOI: 10.35940/ijrte.B1053.0782S519
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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: In cloud platform, parallel computing is precisely one of the methods to handle various computational tasks which need to perform fast on a large dataset. In a system each job was run by the respective processors. Jobs may need to be accompanying through nodes and it will share resources. So scheduling is important to share the resources and path diversity is very much of important in order to get the data within least retrieval time. The existing scheduling algorithms should not efficiently find the optimum solution. In this paper we make a survey to provide the better transfer scheduling algorithm for transfer the data within stipulated time, to maximize the data transfer rate and to choose cost effective paths.
Keywords: Data Center Networks, Parallel Computing, Maximizing Data Transfer, Cloud System.
Scope of the Article: Big Data Quality Validation