AIR: an approximate intelligent redistribution approach to accelerate RAID scaling
AIR: an approximate intelligent redistribution approach to accelerate RAID scaling
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DOI：
10.1007/s42514-020-00021-0
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发表时间：
2020-02
影响因子：
0.9
通讯作者：
Zhehan Lin;Hanchen Guo;Chentao Wu
中科院分区：
文献类型：
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作者：
Zhehan Lin;Hanchen Guo;Chentao Wu
如今，视频和图像正在成为数据存储的主要格式，它们比传统的纯文本占用更多的空间。这种快速增长对大型数据中心的可扩展性提出了很高的要求，其中磁盘阵列（也称为“RAID”）是存储大量数据的主要设备。为了提高RAID系统的可扩展性，提出了几种扩展方法来保证数据的均匀分布，如减少迁移I/O和加快扩展过程。然而，典型的方法是离线的，并且忽略并发应用I/O的影响，并发应用I/O对RAID扩展起着重要作用（即，数据迁移、数据分发等）。例如，磁盘之间的精确均匀的数据分布并不意味着对这些磁盘的均匀I/O访问。为了解决这个问题，在本文中，我们提出了一个近似的智能再分配（AIR）的方法来加速RAID扩展。AIR的主要思想是利用并发应用程序工作负载的动态数据访问模式，并提供近似的数据分布来保证对各个数据磁盘的统一I/O访问。为了实现这一目标，AIR利用流行的机器学习算法来识别应用程序工作负载中的热数据，并提供智能迁移方法来最大限度地减少数据移动。通过这种方式，AIR可以大大减少迁移I/O。为了证明AIR方法的有效性，我们进行了几个模拟通过SIMULINK。结果表明，与传统的RAID扩展方法（如FastScale和GSR）相比，AIR节省了高达99.3%的I/O成本，并将数据迁移率降低了高达95.8%，从而将扩展过程加速了30.3倍。
Nowadays, videos and images are becoming the predominant format of data storage, which take up more space than conventional plain texts. This rapid increment brings a high requirement on scalability in large data centers, where disk arrays (also referred to as “RAID”) are the main devices to store the numerous data. To improve the scalability of RAID systems, several scaling approaches are proposed to guarantee a uniform data distribution, such as decreasing migration I/Os and speeding up the scaling process. However, typical approaches are offline and ignore the impacts of concurrent application I/Os, which plays an important role on RAID scaling (i.e., data migration, data distribution, etc.). For example, an exact uniform data distribution among disks doesn’t mean an even I/O accesses to these disks. To address this problem, in this paper, we propose an approximate intelligent redistribution (AIR) approach to accelerate RAID scaling. The main idea of AIR is utilizing the dynamic data access patterns from concurrent application workloads, and providing an approximate data distribution to guarantee a uniform I/O accesses to various data disks. To achieve this goal, AIR utilizes the prevailing machine learning algorithms to identify hot data from application workloads, and gives an intelligent migration approach to minimize the data movements. By this way, AIR can sharply cut down the migration I/Os. To demonstrate the effectiveness of AIR approach, we conduct several simulations via Disksim. The results show that, compared to traditional RAID scaling approaches such as FastScale and GSR, AIR saves up to 99.3% I/O cost and reduces the data migration ratio by up to 95.8%, which speeds up the scaling process by a factor of up to 30.3X.