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Statistical Characterization of Storage System Workloads for Data Deduplication and Load Placement in Heterogeneous Storage Environments.

机译：异构存储环境中用于重复数据删除和负载放置的存储系统工作负载的统计特性。

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摘要

The underlying technologies for storing digital bits have become more diverse in last decade. There is no fundamental differences in their functionality yet their behaviors can be quite different and no single management technique seems to fit them all. The differences can be categorized based on the metric of interest such as the performance profile, the reliability profile and the power profile.;These profiles are a function of the system and the workload assuming that the systems are exposed only to a pre-specified environment. Near infinite workload space makes it infeasible to obtain the complete profiles for any storage systems unless the system enforces a discrete and finite profile internally. The thesis of this work is that an acceptable approximation of the profiles may be achieved by proper characterization of the workloads. A set of statistical tools as well as understanding of system behavior were used to evaluate and design such characterizations. The correctness of the characterization cannot be fully proved except by showing that the resulting profile can correctly predict any workload and storage system interactions. While this is not possible, we show that we can provide a reasonable confidence in our characterization by statistical evaluation of results.;The characterizations of this work were applied to compression ratio for backup data deduplication and load balancing of heterogeneous storage systems in a virtualized environments. The validation of our characterization is validated through hundreds of real world test cases as well as reasonable deductions based on our understanding of the storage systems. In both cases, the goodness of characterizations were rigorously evaluated using statistical techniques. The findings along the validations were both confirming and contradicting of many previous beliefs.
机译：在过去的十年中，用于存储数字位的基础技术变得更加多样化。它们的功能没有根本的区别，但是它们的行为可以有很大的不同，并且似乎没有一种管理技术适合所有这些。可以根据感兴趣的度量标准对差异进行分类，例如性能配置文件，可靠性配置文件和电源配置文件。这些配置文件是系统和工作负载的函数，假设系统仅暴露在预先指定的环境中。几乎无限的工作负载空间使得无法获得任何存储系统的完整配置文件，除非系统在内部实施了离散且有限的配置文件。这项工作的主题是，通过适当地描述工作负载，可以实现轮廓的可接受近似。使用一组统计工具以及对系统行为的理解来评估和设计此类特征。除了通过显示结果概要文件可以正确预测任何工作负载和存储系统交互之外，无法完全证明表征的正确性。尽管这是不可能的，但我们证明可以通过统计结果评估来为我们的表征提供合理的信心。；这项工作的表征已应用于压缩率，用于虚拟化环境中异构存储系统的备份数据重复数据删除和负载平衡。通过对数百个现实世界中的测试案例以及基于对存储系统的理解得出的合理推论，可以验证我们表征的有效性。在这两种情况下，都使用统计技术严格评估了表征的优劣。验证过程中的发现既证实又与许多先前的信念相矛盾。

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  • 作者

    Park, Nohhyun.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Computer Science.;Engineering Computer.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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