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Presentation of a new method based on modern multivariate approaches for big data replication in distributed environments

机译：基于现代多变量方法在分布式环境中大数据复制的新方法介绍

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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.
机译：随着数据存储和处理的分布式系统的数据和使用增加的数量，减少了复制的数量已经变成了这些系统的关键要求，这已经通过大量研究解决。在本文中，已经提出了一种算法来减少大数据传输中的复制次数，并且最终通过基于已发送的数据类型和最佳地，以有效地和最佳地进行数据，并使用Vikor作为多变量的方法来降低网格上的业务负载排名复制站点的决策。考虑到不同的变量，Vikor方法使得可以在网站评估中占据有效的所有参数。根据结果​​和评估，拟议的方法平均呈现出大约30％的百分比，平均过度在LRU，LFU，BHR和没有代表中。算法。此外，它通过不同的方法改进了现有的多变量方法30％，因为它考虑了有效的参数，例如时间，复制和复制站点的数量，导致复制可能在访问权限方面进行改进。

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