Share This Article:

Availability Importance Measures for Virtualized System with Live Migration

Abstract Full-Text HTML XML Download Download as PDF (Size:1207KB) PP. 359-372
DOI: 10.4236/am.2015.62034    3,009 Downloads   3,461 Views   Citations


This paper presents component importance analysis for virtualized system with live migration. The component importance analysis is significant to determine the system design of virtualized system from availability and cost points of view. This paper discusses the importance of components with respect to system availability. Specifically, we introduce two different component importance analyses for hybrid model (fault trees and continuous-time Markov chains) and continuous-time Markov chains, and show the analysis for existing probabilistic models for virtualized system. In numerical examples, we illustrate the quantitative component importance analysis for virtualized system with live migration.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Zheng, J. , Okamura, H. and Dohi, T. (2015) Availability Importance Measures for Virtualized System with Live Migration. Applied Mathematics, 6, 359-372. doi: 10.4236/am.2015.62034.


[1] Furht, B. and Escalante, A. (2010) Cloud Computing Fundamentals. In Handbook of Cloud Computing, Springer, 3-19.
[2] Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I. and Warfield, A. (2005) Live Migration of Virtual Machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation—Volume 2, USENIX Association, Berkeley, 273-286.
[3] Kundu, S., Rangaswami, R., Dutta, K. and Zhao, M. (2010) Application Performance Modeling in a Virtualized Environment. Proceedings of the 16th IEEE International Symposium on High-Performance Computer Architecture, Bangalore, 9-14 January 2010, 1-10.
[4] Okamura, H., Shigeoka, K., Yamasaki, K., Dohi, T. and Kihara, H. (2012) Performance Evaluation of Cloud Computing in PaaS Environments. Supplemental Proceedings of 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN2012), 36, 122-127.
[5] Cully, B., Lefebvre, G., Meyer, D., Feeley, M., Hutchinson, N. and Warfield, A. (2008) Remus: High Availability via Asynchronous Virtual Machine Replication. Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, USENIX Association, Berkeley, 161-174.
[6] Farr, E., Harper, R., Spainhower, L. and Xenidis, J. (2008) A Case for High Availability in a Virtualized Environment (HAVEN). Proceedings of the 2008 Third International Conference on Availability, Reliability and Security (ARES'08), Barcelona, 4-7 March 2008, 675-682.
[7] Hla Myint, M.T. and Thein, T. (2010) Availability Improvement in Virtualized Multiple Servers with Software Rejuvenation and Virtualization. Proceedings of 4th International Conference on Secure Software Integration and Reliability Improvement, Singapore, 9-11 June 2010, 156-162.
[8] Vishwanath, K.V. and Nagappan, N. (2010) Characterizing Cloud Computing Hardware Reliability. Proceedings of the first ACM Symposium on Cloud Computing (SoCC'10), Indianapolis, 10-11 June 2010, 193-204.
[9] Kim, D.S., Machida, F. and Trivedi, K.S. (2009) Availability Modeling and Analysis of a Virtualized System. Proceedings of the 15th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC-2009), Shanghai, 16-18 November 2009, 365-371.
[10] Matos, R.D.S., Maciel, P.R.M., Machida, F., Kim, D.S. and Trivedi, K.S. (2012) Sensitivity Analysis of Server Virtualized System Availability. IEEE Transactions on Reliability, 61, 994-1006.
[11] Zheng, J., Okamura, H. and Dohi, T. (2012) Component Importance Analysis of Virtualized System. Proceedings of the 9th IEEE International Conference on Autonomic & Trusted Computing (ATC2012), Fukuoka, 4-7 September 2012, 462-469.
[12] Cepin, M. and Mavko, B. (2002) A Dynamic Fault Tree. Reliability Engineering & System Safety, 75, 83-91.
[13] Okamura, H., Zheng, J. and Dohi, T. (2015) Sensitivity Estimation for Markov Reward Models and Its Application to Component Importance Analysis. (In Submission)
[14] Castelli, V., Harper, R.E., Heidelberger, P., Hunter, S.W., Trivedi, K.S., Vaidyanathan, K. and Zeggert, W.P. (2001) Proactive Management of Software Aging. IBM Journal of Research and Development, 45, 311-332.
[15] Trivedi, K.S. (2001) Probability and Statistics with Reliability, Queueing, and Computer Sciences Applications. 2nd Edition, John Wiley & Sons, New York.
[16] Cassady, C.R., Pohl, E.A. and Jin, S. (2004) Managing Availability Improvement Efforts with Importance Measures and Optimization. IMA Journal of Management Mathematics, 15, 161-174.
[17] Birnbaum, Z.W. (1969) On the Importance of Different Components in a Multicomponent System. In: Krishnaiah, P.R., Ed., Multivariate Analysis—II, Academic Press, New York, 581-592.
[18] Lanus, M., Yin, L. and Trivedi, K.S. (2003) Hierarchical Composition and Aggregation of State-Based Availability and Performability Models. IEEE Transactions on Reliability, 52, 44-52.
[19] Strang, G. (2009) Introduction to Linear Algebra. 4th Edition, Wellesley Cambridge Press, Wellesley.

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.