Abstract
As the amounts of data and use of distributed systems for data storage and processing have increased, reducing the number of replications has turned into a crucial requirement in these systems, which has been addressed by plenty of research. In this paper, an algorithm has been proposed to reduce the number of replications in big data transfer and, eventually to lower the traffic load over the grid by classifying data efficiently and optimally based on the sent data types and using VIKOR as a method of multivariate decision-making for ranking replication sites. Considering different variables, the VIKOR method makes it possible to take all the parameters effective in the assessment of site ranks into account. According to the results and evaluations, the proposed method has exhibited an improvement by about thirty percent in average over the LRU, LFU, BHR, and Without Rep. algorithms. Furthermore, it has improved the existing multivariate methods through different approaches to replication by thirty percent, as it considers effective parameters such as time, the number of replications, and replication site, causing replication to occur when it can make an improvement in terms of access.
Publisher
Public Library of Science (PLoS)
Reference27 articles.
1. Full and Partial Replication in Data Grid Using Gridsim.;R. S. Patil;International Journal,2015
2. HGASA: An Efficient Hybrid Technique for Optimizing Data Access in Dynamic Data Grid.;R. K. Grace;In Distributed Computing and Internet Technology,2016
3. Dynamic replication in big data grids using a prediction-based algorithm;M. Beyg Rezayi;First Conference on a Modern Approach to Information and Communications Technology,2013
4. A new fuzzy optimal data replication method for data grid;Z. Ghilavizadeh;Management Science Letters,2013
5. A data replication algorithm for groups of files in data grids;L. Azari;Journal of Parallel and Distributed Computing,2018
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