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Two-mode data distribution scheme for heterogeneous storage in data centers

机译:数据中心异构存储的双模式数据分配方案

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Fast growing "Big Data" demands present new challenges to the traditional distributed storage system solutions. In order to support cloud-scale data centers, new types of distributed storage systems are emerging. They are designed to scale to thousands of nodes, maintain petabytes of data and be highly reliable. The support for virtual machines is also becoming essential as it is one of the most important technology that supports cloud computing. To meet these needs, these distributed storage systems are implemented with advanced data distribution schemes. Data are striped and distributed across the storage cluster based on distribution algorithms instead of mapping tables. The existing algorithms usually balance the data distribution across nodes proportional to their capacity. However, they overlook distinct performance characteristics across different nodes and devices in the emerging heterogeneous storage environment. We propose a two-mode data distribution scheme in this study to maximize the overall performance and keep data balanced across the storage cluster at the same time. The working principle of the two-mode data distribution scheme is provided. We also present a new data read and write strategy to work with the two-mode scheme. We evaluate the computation time for data distribution using two-mode scheme and analyze its implication on the overall IO performance. We expect significant performance improvement while it still needs more analytical and experimental evaluation to further examine the details.
机译:快速增长的“大数据”需求对传统的分布式存储系统解决方案提出了新的挑战。为了支持云规模的数据中心,出现了新型的分布式存储系统。它们旨在扩展到数千个节点,维护PB级数据,并且高度可靠。对虚拟机的支持也变得至关重要,因为它是支持云计算的最重要技术之一。为了满足这些需求,这些分布式存储系统使用高级数据分发方案来实现。根据分发算法(而不是映射表)对数据进行条带化并在整个存储群集中进行分发。现有算法通常会按节点的容量成比例地平衡跨节点的数据分布。但是,它们忽略了新兴异构存储环境中不同节点和设备之间不同的性能特征。在本研究中,我们提出了一种双模式数据分发方案,以最大程度地提高整体性能并同时在整个存储群集中保持数据平衡。提供了两种模式的数据分配方案的工作原理。我们还提出了一种新的数据读写策略,可用于双模式方案。我们使用双模式方案评估数据分发的计算时间,并分析其对整体IO性能的影响。我们希望性能得到显着改善,同时仍然需要更多的分析和实验评估来进一步检查细节。

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