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Progressive Data Retrieval for Distributed Networked Storage

机译:分布式网络存储的渐进式数据检索

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We propose a decentralized progressive data retrieval (PDR) mechanism for data reconstruction in a network of Byzantine and crash-stop nodes. The scheme progressively retrieves stored data, such that it achieves the minimum communication cost possible. In particular, PDR gracefully adapts the cost of successful data retrieval to the number of Byzantine and crash-stop storage nodes. At the core of PDR is an incremental Reed-Solomon decoding (IRD) procedure that is highly computation efficient for data reconstruction. IRD's computation efficiency arises from its ability to utilize intermediate computation results. In addition, we provide an in-depth analysis of PDR and compare it to decentralized erasure coding and decentralized fountain coding algorithms for distributed storage systems. Moreover, our implementation results show that PDR has up to 35 times lower computation time over the state-of-the-art error-erasure decoding scheme for distributed storage systems. In our analysis, we also show that the code structure of PDR and the number of available storage nodes are independent of each other, and they can be used to control both the data dissemination and retrieval complexity.
机译:我们提出了一种分散式渐进数据检索(PDR)机制,用于在拜占庭式和崩溃停止节点的网络中进行数据重建。该方案逐步检索存储的数据,从而实现可能的最低通信成本。特别是,PDR可以根据拜占庭式和崩溃停止存储节点的数量适当地调整成功数据检索的成本。 PDR的核心是增量Reed-Solomon解码(IRD)过程,该过程对数据重建具有很高的计算效率。税务局的计算效率源于其利用中间计算结果的能力。另外,我们提供了对PDR的深入分析,并将其与分布式存储系统的分散擦除编码和分散喷泉编码算法进行了比较。此外,我们的实施结果表明,PDR的计算时间比分布式存储系统最新的纠错解码方案低35倍。在我们的分析中,我们还显示了PDR的代码结构和可用存储节点的数量彼此独立,并且可用于控制数据分发和检索的复杂性。

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