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Self-managing techniques for storage resource management.

机译：用于存储资源管理的自我管理技术。

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摘要

The increasing reliance on online information in our daily lives had called for a rethinking of how people manage and maintain computer systems. As information has become more valuable and computing environments more complex, improved manageability has become key to ensuring availability. The sheer size of enterprise-scale storage systems coupled with the diversity and variability of application workloads makes their management non-trivial. Not surprisingly, numerous studies have shown that management costs have become a significant fraction of the total cost of ownership of large storage systems. Traditionally storage management tasks have been performed manually by administrators using a combination of experience, rules of thumb and trial and error. This increases the chance of a misconfigured or sub-optimally configured system. The cost of such misconfigurations can be high, since even a short downtime can result in substantial revenue losses. So, although storage is cheap, storage management is costly and storage mismanagement costlier. This argues the need for an automated, seamless and intelligent way to manage the storage resource.; In this thesis, I propose self-managing techniques, specifically for resource management, to improve the manageability of large-scale storage systems. I have focused on techniques for automating two common storage allocation tasks: storage bandwidth allocation and storage space allocation. Large scale storage systems host data objects of multiple types which are accessed by applications with diverse service requirements. I have developed an online measurement based technique as well as one based on learning to dynamically partition bandwidth between application classes. Storage allocation algorithms that determine object placement, and thus the performance, are crucial to the success of a storage system. For a self-managing storage system a suitable placement technique is one that has low management overhead and delivers agreeable performance. In this context, I empirically compare different placement techniques to determine their suitability for large-scale storage systems, Finally, I also present techniques to minimize the amount of data displaced when remapping objects to eliminate hotspots.
机译：在我们的日常生活中，对在线信息的日益依赖要求对人们如何管理和维护计算机系统进行重新思考。随着信息变得越来越有价值，计算环境越来越复杂，改善的可管理性已成为确保可用性的关键。企业级存储系统的庞大规模，加上应用程序工作负载的多样性和可变性，使得它们的管理非常重要。毫不奇怪，大量研究表明，管理成本已成为大型存储系统总拥有成本的很大一部分。传统上，存储管理任务是由管理员结合经验，经验法则和反复试验来手动执行的。这增加了配置错误或配置欠佳的系统的机会。这种配置错误的代价可能很高，因为即使是很短的停机时间也可能导致大量的收入损失。因此，尽管存储价格便宜，但是存储管理成本很高，而存储管理不善的成本也更高。这表明需要一种自动，无缝和智能的方式来管理存储资源。在本文中，我提出了专门针对资源管理的自我管理技术，以提高大型存储系统的可管理性。我专注于使两种常见的存储分配任务自动化的技术：存储带宽分配和存储空间分配。大型存储系统托管多种类型的数据对象，这些数据对象可以由具有不同服务需求的应用程序访问。我已经开发了一种基于在线测量的技术以及一种基于学习的方法来动态划分应用程序类之间的带宽。确定对象放置并进而确定性能的存储分配算法对于存储系统的成功至关重要。对于自管理存储系统，一种合适的放置技术应具有较低的管理开销并提供令人满意的性能。在这种情况下，我根据经验比较了不同的放置技术，以确定它们是否适合大型存储系统。最后，我还提出了一些技术，可在重新映射对象以消除热点时最大程度地减少数据移位量。

著录项

  • 作者

    Sundaram, Vijay.;

  • 作者单位

    University of Massachusetts Amherst.;

  • 授予单位 University of Massachusetts Amherst.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 176 p.
  • 总页数 176
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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