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Building Reliable Storage Clouds: Models, Fundamental Tradeoffs, and Solutions

机译：构建可靠的存储云：模型，基本权衡和解决方案

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Distributed storage has been an active research area for decades. With the proliferation of cloud computing, there has been a rejuvenated interest in two perspectives. The first perspective is seen through the lenses of the cloud providers: how should we build global storage services for cloud hosted services and applications at scale with high reliability and availability guarantees, but also in a cost effective way? The second perspective is seen through the lenses of the service providers that utilize public clouds: how can we achieve high I/O performance over cloud storage within a cost budget? In this manuscript, we first present various kinds of distributed storage systems, their operational characteristics and the key techniques to improve their performance. We then focus on cloud storage, exclusively. Cloud storage has massive scales with the promise to provide as much storage capacity as their tenants demand. Cloud providers also promise very high durability, availability, and I/O performance. In this context, we cover the fundamental tradeoffs between storage efficiency and network bandwidth as well as I/O throughput and latency. Erasure codes play an essential role in these tradeoffs and, thus, we also present their design and usage in the context of cloud storage broadly. We pay particular attention on various queuing models and the corresponding performance analysis in the presence of coded storage. We provide exact and approximate solutions under various settings and assumptions. We describe optimal or near-optimal scheduling and coding strategies that are established based on these analyses.
机译：数十年来，分布式存储一直是活跃的研究领域。随着云计算的激增，人们从两个角度重新焕发了兴趣。从云提供商的角度来看第一个观点：我们应该如何为云托管服务和应用程序构建具有高可靠性和可用性保证的大规模存储服务，同时又要以经济高效的方式来构建全局存储服务？通过使用公共云的服务提供商的视角可以看到第二个角度：我们如何在成本预算内通过云存储实现高I / O性能？在本手稿中，我们首先介绍各​​种分布式存储系统，它们的操作特性以及提高其性能的关键技术。然后，我们专门专注于云存储。云存储具有巨大的规模，并有望提供与租户需求一样多的存储容量。云提供商还承诺非常高的耐用性，可用性和I / O性能。在这种情况下，我们介绍了存储效率和网络带宽以及I / O吞吐量和延迟之间的基本权衡。擦除代码在这些折衷方案中起着至关重要的作用，因此，我们还广泛地在云存储的上下文中介绍了它们的设计和使用。在存在编码存储的情况下，我们特别关注各种排队模型和相应的性能分析。我们提供各种设置和假设下的精确和近似解决方案。我们描述了基于这些分析而建立的最佳或接近最佳的调度和编码策略。

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