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Study on Replica Strategy Based on Access Pattern Mining in Smart City Cloud Storage System

机译:基于智能城市云存储系统访问模式挖掘的复制品策略研究

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摘要

The replica strategy in traditional distributed file system, which creates a copy mainly from the perspective of internal resources while changes in external demand are ignored. However, this strategy is not suitable for deployment in a service-based, resource-rich internal storage “smart city” in cloud storage center. This paper proposes a replica strategy, which combines data security (the minimum amount of copies) together with service needs (best copy volume). The strategy predicts file popularity based on access pattern mining algorithms. What’s more, the number of copies of the cloud adjusts itself dynamically according to the popularity of file and system resources. Mining algorithm is based on the analysis of the characteristics of spatio-temporal data in smart cities. The algorithm first maps the historical user access request to the spatio-temporal attribute domain. Then according to the geographical area grid and association rules, the correlation analysis and evolution rule identification of access requests are carried out in the domain of spatio-temporal attributes. Finally dig out the user access mode and predict the user’s access request, calculate the file popularity according to the request. The simulation results show that the popularity of the file calculated by the access pattern mining algorithm in this paper is simple and efficient, and the prediction accuracy of the popularity can reach 84%. The dynamic replica mechanism based on popularity has a significant advantage in coping with sudden large-scale concurrent accesses. Meanwhile, compared with the conventional dynamic replicas based on access frequency, the proposed strategy consumes less storage resources.
机译:传统分布式文件系统中的副本策略,主要从内部资源的角度创建副本,而忽略外部需求的变化。但是,此策略不适合在云存储中心的基于服务的资源丰富的内部存储“智能城市”中部署。本文提出了一种副本策略,它将数据安全性(最小份数)与服务需求(最佳复制卷)相结合。该策略基于访问模式挖掘算法预测文件普及。更重要的是,云的副本数根据文件和系统资源的普及而动态调整自身。挖掘算法基于智能城市时空数据的特征分析。该算法首先将历史用户访问请求映射到Spatio-Temporal属性域。然后根据地理区域网格和关联规则,在时空属性的域中执行访问请求的相关分析和演进规则识别。最后挖掘用户访问模式并预测用户的访问请求,根据请求计算文件流行度。仿真结果表明,本文中访问模式挖掘算法计算的文件的普及简单富有高,普及的预测精度可以达到84%。基于流行度的动态副本机制在应对突然的大规模并发访问方面具有显着的优势。同时,与基于访问频率的传统动态副本相比,所提出的策略消耗了较少的存储资源。

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