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An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment

An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment

K. Sasikumar, B. Vijayakumar
Copyright: © 2020 |Volume: 16 |Issue: 1 |Pages: 23
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781799805014|DOI: 10.4018/IJEIS.2020010104
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MLA

Sasikumar, K., and B. Vijayakumar. "An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment." IJEIS vol.16, no.1 2020: pp.69-91. http://doi.org/10.4018/IJEIS.2020010104

APA

Sasikumar, K. & Vijayakumar, B. (2020). An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment. International Journal of Enterprise Information Systems (IJEIS), 16(1), 69-91. http://doi.org/10.4018/IJEIS.2020010104

Chicago

Sasikumar, K., and B. Vijayakumar. "An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment," International Journal of Enterprise Information Systems (IJEIS) 16, no.1: 69-91. http://doi.org/10.4018/IJEIS.2020010104

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Abstract

The main aim of the proposed methodology is to design a multi-objective function for replica management system using oppositional gravitational search algorithm (OGSA), in which we analyze the various factors influencing replication decisions such as mean service time, mean file availability, energy consumption, load variance, and mean access latency. The OGSA algorithm is hybridization of oppositional-based learning (OBL) and gravitational search algorithm (GSA), which is change existing solution, and to adopt a new good solution based on objective function. Here, firstly we create a set of files and data node to generate a population by assigning the file to data node randomly and evaluate the fitness which is minimizing the objective function. Secondly, we regenerate the population to produce optimal or suboptimal population using OGSA. The experimental results show that the performance of the proposed methods is better than the other methods of data replication problem.

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