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A Social Aware Approach for Online Social Networks Data Allocation and Replication

机译:在线社交网络数据分配和复制的一种社交感知方法

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Data in Cloud Computing should be stored and replicated across different geo-located data centers for high data availability. Facebook, Twitter and many other social media are examples of booming Cloud-based web applications; known as Online Social Networks (OSN). These OSN services started in the first decade of the twentieth century and with an exponential increase of the amount of shared data and number of users. Data storage and replication for this massive amount of data is a hot research topic especially for social networks. In social networks, the user writes her/his data while most of the reads is done by her/his active friends; not by himself as usually happens in other web applications. Each OSN has several datacenters geo-distributed across the globe to provide the social media service for the worldwide users. In this paper, we propose a novel data storage and replica algorithms across a group of cloud data-centers based on the friendship matrix and their spatial distribution. The extensive simulation studies that have been conducted to compare the proposed algorithm with the famous Cassandra rack unaware algorithm, which is currently used in Facebook and Twitter, report the outstanding performance of the proposed algorithm in terms of decreasing the average user's access delay while preserving an acceptable load balance.
机译:云计算中的数据应在不同地理位置的数据中心中存储和复制,以实现高数据可用性。 Facebook,Twitter和许多其他社交媒体是蓬勃发展的基于云的Web应用程序的例子。被称为在线社交网络(OSN)。这些OSN服务始于20世纪的前十年,并且共享数据量和用户数量呈指数级增长。对于海量数据的数据存储和复制是一个热门的研究主题,尤其是对于社交网络。在社交网络中,用户写入她/他的数据,而大多数读取是由她/他的活跃朋友完成的;不能像其他Web应用程序中通常那样单独使用。每个OSN都具有分布在全球各地的多个数据中心,以为全球用户提供社交媒体服务。在本文中,我们基于友谊矩阵及其空间分布,提出了一种跨一组云数据中心的新颖数据存储和复制算法。为了将拟议的算法与著名的Cassandra机架式无感知算法(目前在Facebook和Twitter中使用)进行了广泛的仿真研究,报告了该算法在降低平均用户访问延迟的同时保持了良好的性能方面的出色性能。可接受的负载平衡。

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