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A replica management strategy based on MOEA/D

机译:基于MOEA / D的副本管理策略

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As distributed storage systems have been used more and more widely nowadays, data replication management has become a hot research topic. Although replica technology can increase data availability and improve load balancing, an unreasonable replica management strategy will waste too much storage and increase the energy consuming sharply. In this paper, we developed a replica management strategy based on MOEA/D, in which we take five factors affecting replication decisions into consideration such as system data unavailability, network traffic, disk performance, load balancing and energy consuming. By using the improved MOEA/D algorithm, we get a Pareto solution sets, then an elite selection strategy based on individual density was proposed to pick out the suitable solution from the set. At last, we adjust the replica dynamically depending on the file access record log. Those steps are gathered to form the final strategy: A Replica Management Strategy Based On MOEA/D(MDRMS). Series of experiments are designed to demonstrate the validity and universality of the strategy. The experimental results show that MDRMS performs better than HDFS(Hadoop Distributed File System) and DRPMO (Dynamic Replica Placement based on Multi-objective Optimization), especially in load balancing and energy consuming.
机译:如今,随着分布式存储系统的使用越来越广泛,数据复制管理已成为研究的热点。尽管副本技术可以提高数据可用性并改善负载平衡,但是不合理的副本管理策略将浪费过多的存储空间并急剧增加能耗。在本文中,我们开发了基于MOEA / D的副本管理策略,其中考虑了影响复制决策的五个因素,例如系统数据不可用,网络流量,磁盘性能,负载平衡和能耗。通过使用改进的MOEA / D算法,我们得到了帕累托解集,然后提出了基于个体密度的精英选择策略,以从集合中选择合适的解。最后,我们根据文件访问记录日志动态调整副本。收集这些步骤以形成最终策略:基于MOEA / D(MDRMS)的副本管理策略。设计了一系列实验以证明该策略的有效性和普遍性。实验结果表明,MDRMS的性能优于HDFS(Hadoop分布式文件系统)和DRPMO(基于多目标优化的动态副本放置),尤其是在负载平衡和能耗方面。

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