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Minimizing data redundancy for high reliable cloud storage systems

机译:最大限度地减少数据冗余,以实现高度可靠的云存储系统

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摘要

Cloud storage system provides reliable service to users by widely deploying redundancy schemes in its system - which brings high reliability to the data storage, but inversely introduces significant overhead to the system, consisting of storage cost and energy consumption. The core behind this issue is how to leverage the relationship between data redundancy and data reliability. To optimize both concurrently is apparently difficult. As such, to fix one as a constraint and then to reach another one becomes the consensus. We aim in the paper to pursue a storage allocation scheme that minimizes the data redundancy while achieving a given (high) data reliability. For this purpose, we have provided a novel model based on generating function. With this model, we have proposed a practical and efficient storage allocation scheme, which is proved to be able to minimize the data redundancy. We analytically demonstrate that the suggested solution brings several advantages, in particular the reduction of the search space and the acceleration to the computation. We also assess the improvement on the savings of data redundancy experimentally by adopting availability traces collected from real world - which encouragingly shows that the reduction of data redundancy by our solution can reach up to more than 30% as compared to the heuristic method recently proposed in the research community. (C) 2015 Elsevier B.V. All rights reserved.
机译:云存储系统通过在系统中广泛部署冗余方案,为用户提供了可靠的服务-这给数据存储带来了高可靠性,但反过来给系统带来了巨大的开销,包括存储成本和能耗。该问题背后的核心是如何利用数据冗余和数据可靠性之间的关系。同时优化两者显然很困难。这样,将一个固定为约束然后达到另一个成为共识。在本文中,我们旨在追求一种存储分配方案,该方案可在实现给定(高)数据可靠性的同时最大程度地减少数据冗余。为此,我们提供了一种基于生成函数的新颖模型。使用此模型,我们提出了一种实用而有效的存储分配方案,该方案被证明能够最小化数据冗余。我们通过分析证明,所提出的解决方案具有多个优点,特别是减少了搜索空间并加快了计算速度。我们还通过采用从现实世界中收集到的可用性跟踪数据,通过实验评估了数据冗余节省方面的改进-令人鼓舞的是,与最近在2000年提出的启发式方法相比,我们的解决方案减少的数据冗余可以达到30%以上。研究社区。 (C)2015 Elsevier B.V.保留所有权利。

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