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A Security-Aware Data Placement Mechanism for Big Data Cloud Storage Systems

机译:大数据云存储系统的安全感知数据放置机制

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Public clouds have become an attractive candidate to meet the ever-growing storage demands. However, storing data in public clouds increases data retrieval time and threat level for data security. These challenges drive the need for intelligent methods that solve the data placement problem to achieve high performance while satisfying the security requirement. In this paper, we propose a novel approach for data placement in cloud storage systems addressing the above challenges. With the security constraint, we first formulate the data placement problem as a linear programming model that minimizes the total retrieval time of a data, which is divided and distributed over storage nodes. We then develop a heuristic algorithm namely Security-awarE Data placement mechanism for cLOUd storage Systems (SEDuLOUS) to solve the problem. We demonstrate the effectiveness of the proposed algorithm through comprehensive simulations. The simulation results show that the proposed algorithm significantly reduces the retrieval time by up to 20% for the random-network-topology systems and 19% for the Internet2-topology system compared to baseline methods, which consider only the security requirement.
机译:公共云已成为满足不断增长的存储需求的有吸引力的候选人。但是,在公共云中存储数据会增加数据安全的数据检索时间和威胁级别。这些挑战推动了解决数据放置问题的智能方法,以实现高性能,同时满足安全要求。在本文中,我们提出了一种用于解决上述挑战的云存储系统中的数据放置方法。通过安全约束,我们首先将数据放置问题作为线性编程模型,最小化数据的总检索时间,该数据被分割和分发在存储节点上。然后,我们开发了一种启发式算法,即用于云存储系统(SEDULUD)来解决问题的安全感知数据放置机制。我们通过全面模拟展示所提出的算法的有效性。仿真结果表明,与基线方法相比,所提出的算法对于随机网络拓扑系统的检索时间明显降低了可随机网络拓扑系统的20%,而Internet2-Topology System将仅考虑安全要求。

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