阵列模糊性能分析

G. Navarro, M. Manic
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引用次数: 8

摘要

以前已经对磁盘阵列系统的性能进行了研究。然而,在数据不精确的情况下,模糊模型可以作为性能分析的基础。本文采用马尔可夫奖励模型(MRM)对磁盘阵列的性能进行了分析。该模型认为修复是冗余的重建(rebuild),而不是硬盘的更换。使用传统的、简洁的算法,对于单个模型参数的每次变化,模型都需要再次运行,从而导致难以解释的一系列曲线。在本文提出的方法中,MRM的每个状态的奖励以及磁盘阵列的其他参数都用模糊数表示。采用模糊算法对磁盘阵列进行性能估计,具有显著的优越性。首先,该模型能够捕获每个模型参数的不确定性方差。其次,与传统的、简洁的算法方法不同,该模型通过单次运行就能估计出系统性能的上下边界
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Performability Analysis of Disk Arrays
The performability of disk arrays systems has been studied before. However, in the case of imprecise data, a fuzzy model can be the base for the performability analysis. In this paper a performability analysis of a disk array using a Markov reward model (MRM) is presented. The model considers the repair as the reconstruction (rebuild) of the redundancy, not as a hard drive replacement. With traditional, crisp arithmetic, for each change in a single model parameter the model would need to be run again, resulting in a family of curves difficult to interpret. In the approach presented in this paper, the rewards for each of the states of the MRM, as well as other disk array parameters are expressed through fuzzy numbers. The use of fuzzy arithmetic for the performability estimation of a disk array proved significant advantages. First, the model was able to capture the uncertainty variance of each of the model parameters. Secondly, as opposed to traditional, crisp arithmetic approach, the presented model provides the estimation of the lower and upper boundary of the system performability with a single run of the model
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