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Sensitivity analysis of reliability and performability measures for multiprocessor systems

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K. S. TrivediAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 16, Issue 1

Pages 177 - 186

https://doi.org/10.1145/1007771.55616

Published: 01 May 1988 Publication History

Abstract

Traditional evaluation techniques for multiprocessor systems use Markov chains and Markov reward models to compute measures such as mean time to failure, reliability, performance, and performability. In this paper, we discuss the extension of Markov models to include parametric sensitivity analysis. Using such analysis, we can guide system optimization, identify parts of a system model sensitive to error, and find system reliability and performability bottlenecks.

As an example we consider three models of a 16 processor. 16 memory system. A network provides communication between the processors and the memories. Two crossbar-network models and the Omega network are considered. For these models, we examine the sensitivity of the mean time to failure, unreliability, and performability to changes in component failure rates. We use the sensitivities to identify bottlenecks in the three system models.

References

[1]

M. Avriel. Nonlinear Programming: Analysis and Methods. Prentice-Hall, Englewood Cliffs, N J, 1976.

[2]

M. D. Beaudry. Performance Related Reliability for Computer Systems. IEEE Transactions on Computers, C-27(6):540-547, June 1978.

Digital Library

[3]

D. P. Bhandarkar. Analysis of Memory interference in Multiprocessors. IEEE Transactz'ons on Computers, C-24(9):897-908, September 1975.

Digital Library

[4]

C. R. Das and L. N. Bhuyan. Reliability Simulation of Multiprocessor Systems. In Proceedings of the International Conference on Parallel Processing, pages 591-598, August 1985.

[5]

P. M. Frank. Introduction to System Sensitivity Theory. Academic Press, New York, 1978.

[6]

A. Goyal, S. Lavenberg, and K. Trivedi. Probabilistic Modeling of Computer System Availability. Annals of Operations Research, 8:285-306, March 1987.

[7]

P. Heidelberger and A. Goyal. Sensitivity Analysis of Continuous Time Markov Chains Using Uniformization. In P. J. Courtois G. Iazeolla and O. J. Boxma, editors, Proceedings o} the 2nd International Workshop on Applied Mathematics and Performance/Reliability Models of Computer/Communication Systems, pages 93-104, Rome, Italy, May 1987.

[8]

R. A. Howard. Dynamic Probabilistic Systems, Volume II: Semi-Markov and Decision Processes. John Wiley and Sons, New York, 1971.

[9]

J. G. Kemeny and J. L. Snell. Finite Markov Chains. Van Nostrand-Reinhold, Princeton, NJ, 1960.

[10]

D. Kuck. The Structure of Computers and Computations. Volume 1, John Wiley and Sons, NY, 1978.

Digital Library

[11]

D. H. Lawrie. Access and Alignment of Data in an Array Processor. IEEE Transactions on Computers, C-24:1145-1155, December 1975.

Digital Library

[12]

R. A. Marie, A. L. Reibman, and K. S. Trivedi. Transient Solution of Acyclic Markov Chains. Performance Evaluation, 7(3):175-194, 1987.

Digital Library

[13]

J. F. Meyer. On Evaluating the Performability of Degradable Computing Systems. IEEE Transactions on Computers, C-29(8):720-731, August 1980.

Digital Library

[14]

J. H. Patel. Performance of Processor-Memory Interconnections for Multiprocessors. IEEE Transactions on Computers, C-30(10):771-780, October 1981.

Digital Library

[15]

A. Reibman and K. Trivedi. Numerical Transient Analysis of Markov Models. Computers and Operations Research, 15 (1): 19-36, 1988.

Digital Library

[16]

A. L. Reibman and K. S. Trivedi. Transient Analysis of Cumulative Measures of Markov Chain Behavior. 1987. Submitted for publication.

[17]

D. P. Siewiorek. Multiprocessors: Reliability Modeling and Graceful Degradation. In Infotech State of the Art Conference on System Reliability, pages 48-73, Infotech International, London, 1977.

[18]

D. P. $iewiorek, V. Kini, R. Joobbani, and H. Bellis. A Case Study of C.mmp, Cm*, and C.vmp: Part iI m Predicting and Calibrating Reliability of Multiprocessor. Proceedings of the IEEE, 66(10):1200-1220, October 1978.

[19]

M. Smotherman. Parametric Error Analysis and Coverage Approximations in Reliability Modeling. PhD thesis, Department of Computer Science, University of North Carolina, Chapel Hill, NC, 1984.

Digital Library

[20]

M. Smotherman, R. Geist, and K. Trivedi. Provably Conservative Approximations to Complex Reliability Models. IEEE Transactions on Computers, C-35(4):333-338, April 1986.

Digital Library

[21]

W. Stewart and A. Goyal. Matrix Methods in Large Dependability Models. Research Report RC-11485, IBM, November 1985.

[22]

K. S. Trivedi. Probability and Statistics with Reliability, Queueing and Computer Science Applications. Prentice-Hall, Englewood Cliffs, NJ, 1982.

Digital Library

[23]

R. S. Varga. Matrix Iterative Analysis. Prentice- Hall, Englewood Cliffs, NJ, 1962.

Cited By

Chang XShi YZhang ZXu ZTrivedi K(2022)Job Completion Time Under Migration-Based Dynamic Platform TechniqueIEEE Transactions on Services Computing10.1109/TSC.2020.298921515:3(1345-1357)Online publication date: 1-May-2022
https://doi.org/10.1109/TSC.2020.2989215
Bachi KAbbas KHeidergott B(2021)Statistical Taylor series expansion: An approach for epistemic uncertainty propagation in Markov reliability modelsRAIRO - Operations Research10.1051/ro/201909155(S593-S624)Online publication date: 2-Mar-2021
https://doi.org/10.1051/ro/2019091
Trivedi KBobbio A(2020)Reliability and Availability Analysis in PracticeHandbook of Advanced Performability Engineering10.1007/978-3-030-55732-4_22(501-522)Online publication date: 17-Nov-2020
https://doi.org/10.1007/978-3-030-55732-4_22
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Sensitivity analysis of reliability and performability measures for multiprocessor systems

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Sensitivity analysis of reliability and performability measures for multiprocessor systems
SIGMETRICS '88: Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems

Traditional evaluation techniques for multiprocessor systems use Markov chains and Markov reward models to compute measures such as mean time to failure, reliability, performance, and performability. In this paper, we discuss the extension of Markov ...
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We analyze the computation of sensitivities in network reliability analysis. The associated models are graphs whose components are weighted by probabilities (their reliabilities) and they are widely used, for instance, in the design of communication ...

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 16, Issue 1

May 1988

266 pages

ISSN:0163-5999

DOI:10.1145/1007771

Issue’s Table of Contents

SIGMETRICS '88: Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
May 1988
282 pages
ISBN:0897912543
DOI:10.1145/55595
Chairman:
C. U. Smith
L&S Computer Technology

Copyright © 1988 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 May 1988

Published in SIGMETRICS Volume 16, Issue 1

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Cited By

Chang XShi YZhang ZXu ZTrivedi K(2022)Job Completion Time Under Migration-Based Dynamic Platform TechniqueIEEE Transactions on Services Computing10.1109/TSC.2020.298921515:3(1345-1357)Online publication date: 1-May-2022
https://doi.org/10.1109/TSC.2020.2989215
Bachi KAbbas KHeidergott B(2021)Statistical Taylor series expansion: An approach for epistemic uncertainty propagation in Markov reliability modelsRAIRO - Operations Research10.1051/ro/201909155(S593-S624)Online publication date: 2-Mar-2021
https://doi.org/10.1051/ro/2019091
Trivedi KBobbio A(2020)Reliability and Availability Analysis in PracticeHandbook of Advanced Performability Engineering10.1007/978-3-030-55732-4_22(501-522)Online publication date: 17-Nov-2020
https://doi.org/10.1007/978-3-030-55732-4_22
Liu BChang XHan ZTrivedi KRodrguez R(2018)Model-based sensitivity analysis of IaaS cloud availabilityFuture Generation Computer Systems10.1016/j.future.2017.12.06283:C(1-13)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1016/j.future.2017.12.062
Silva BMatos RTavares EMaciel PZimmermann A(2018)Sensitivity analysis of an availability model for disaster tolerant cloud computing systemInternational Journal of Network Management10.1002/nem.204028:6(e2040)Online publication date: 18-Jul-2018
https://doi.org/10.1002/nem.2040
Pinciroli RTrivedi KBobbio A(2017)Parametric Sensitivity and Uncertainty Propagation in Dependability Modelsproceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools on 10th EAI International Conference on Performance Evaluation Methodologies and Tools10.4108/eai.25-10-2016.2266529(44-51)Online publication date: 3-May-2017
https://dl.acm.org/doi/10.4108/eai.25-10-2016.2266529
Zheng ZTrivedi KQiu KXia R(2017)Semi-Markov Models of Composite Web Services for their Performance, Reliability and BottlenecksIEEE Transactions on Services Computing10.1109/TSC.2015.247595710:3(448-460)Online publication date: 1-May-2017
https://doi.org/10.1109/TSC.2015.2475957
Trivedi KBobbio A(2017)Hierarchical ModelsReliability and Availability Engineering10.1017/9781316163047.022(577-630)Online publication date: 30-Aug-2017
https://doi.org/10.1017/9781316163047.022
Trivedi KBobbio A(2017)Multi-Level ModelsReliability and Availability Engineering10.1017/9781316163047.021(575-576)Online publication date: 30-Aug-2017
https://doi.org/10.1017/9781316163047.021
Trivedi KBobbio A(2017)Continuous-Time Markov Chain: Availability ModelsReliability and Availability Engineering10.1017/9781316163047.013(305-356)Online publication date: 30-Aug-2017
https://doi.org/10.1017/9781316163047.013
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