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Seventh Pacific Rim International Symposium on Dependable Computing (PRDC'00)
Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule
Los Angeles, California
December 18-December 20
ISBN: 0-7695-0975-4
ASCII Text
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T. Dohi, K. Goseva-Popstojanova, K.S. Trivedi, "Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule," Pacific Rim International Symposium on Dependable Computing, IEEE, pp. 77, Seventh Pacific Rim International Symposium on Dependable Computing (PRDC'00), 2000.
 
 
BibTex
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@article{ 10.1109/PRDC.2000.897287,
author = {T. Dohi and K. Goseva-Popstojanova and K.S. Trivedi},
title = {Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule},
journal ={Pacific Rim International Symposium on Dependable Computing, IEEE},
volume = {0},
year = {2000},
isbn = {0-7695-0975-4},
pages = {77},
doi = {http://doi.ieeecomputersociety.org/10.1109/PRDC.2000.897287},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pacific Rim International Symposium on Dependable Computing, IEEE
TI - Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule
SN - 0-7695-0975-4
SP
EP
A1 - T. Dohi,
A1 - K. Goseva-Popstojanova,
A1 - K.S. Trivedi,
PY - 2000
KW - software maintenance; nonparametric statistics; software reliability; optimal software rejuvenation; software rejuvenation; semi-Markov processes; nonparametric statistical algorithms; statistical estimation
VL - 0
JA - Pacific Rim International Symposium on Dependable Computing, IEEE
ER -
 
T. Dohi, Dept. of Ind. & Syst. Eng., Hiroshima Univ., Japan
K. Goseva-Popstojanova, Dept. of Ind. & Syst. Eng., Hiroshima Univ., Japan
K.S. Trivedi, Dept. of Ind. & Syst. Eng., Hiroshima Univ., Japan
In this paper, we extend the classical result by Huang, Kintala, Kolettis and Fulton (1995), and in addition propose a modified stochastic model to determine the software rejuvenation schedule. More precisely, the software rejuvenation models are formulated via the semi-Markov processes, and the optimal software rejuvenation schedules which maximize the system availabilities are derived analytically for respective cases. Further, we develop nonparametric statistical algorithms to estimate the optimal software rejuvenation schedules, provided that the statistical complete (unsensored) sample data of failure times is given. In numerical examples, we examine asymptotic properties for the statistical estimation algorithms.
Index Terms:
software maintenance; nonparametric statistics; software reliability; optimal software rejuvenation; software rejuvenation; semi-Markov processes; nonparametric statistical algorithms; statistical estimation
Citation:
T. Dohi, K. Goseva-Popstojanova, K.S. Trivedi, "Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule," prdc, pp.77, Seventh Pacific Rim International Symposium on Dependable Computing (PRDC'00), 2000
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