ASSESSMENT OF COMPUTING SYSTEMS RELIABILITY BY NON-PARAMETRIC METHOD BY SMALL SAMPLES OF OPERATIONAL DATA
Vladimir S. Nikulin, Andrey I. Pestunov
Novosibirsk State University of Economics and Management, Novosibirsk, Russian Federation
Keywords: reliability of computing systems, operational data, machine learning, Rosenblatt-Parzen method, smoothing parameter, failure distribution density
Abstract
A new method for assessing the reliability of small computing systems that allows the generation of only small samples of operational data is proposed in the article. The result of applying the technique is the posterior failure distribution density, on the basis of which various reliability indicators can be calculated. The methodology consists of two stages: the first is the preparation of operational data, including detection of failures using machine learning methods, and the second is the construction of the failure distribution density using the adapted Rosenblatt-Parzen method. Increasing the efficiency of estimates using the proposed method is achieved by taking into account censored data, compensating for the shift of failure distribution densities and finding the optimal smoothing parameter.
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