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Inducing Models of Black-Box Storage Arrays

Kelly, Terence; Cohen, Ira; Goldszmidt, Moises; Keeton, Kimberly


Keyword(s): statistical model induction; storage arrays; I/O response time prediction; performance model induction

Abstract: This paper applies statistical model-induction techniques to the problem of forecasting response times in storage systems. Our work differs from prior research in several ways: we regard storage systems as black boxes; we automatically induce models rather than constructing them from detailed expert knowledge; we use lightweight passive observations, rather than extensive controlled experiments, to collect input data; we forecast individual response times rather than aggregates or averages; and we focus on large and complex enterprise storage arrays that comprise many RAID groups. We evaluate our methods using a lengthy storage trace collected in a real-world environment, and measure the predictive value of information available when requests are issued. This paper makes several contributions. First, we quantify the potential of a class of statistical methods for the challenging problem of automatic performance model induction. Second, we quantify improvements in accuracy that result when the range of information available to our models increases. Finally, we describe a general, low-cost modeling methodology that can be applied to a wide range of storage arrays.

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