Username   Password       Forgot your password?  Forgot your username? 


Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006


State-Control-Limit-Based Rejuvenation Modeling of Virtualized Cloud Server

Volume 14, Number 3, March 2018, pp. 473-782
DOI: 10.23940/ijpe.18.03.p8.473482

Weichao Danga,b and Jianchao Zengb,c

aCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
bDivision of Industrial and System Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China
cSchool of Computer Science and Control Engineering, North University of China, Taiyuan, 030051, China

(Submitted on November 15, 2017; Revised on January 21, 2018; Accepted on February 19, 2018)


Software rejuvenation modeling of the virtualized Cloud Server has been studied. A software rejuvenation policy on the virtual machines and the virtual machine monitor has been proposed in order to ensure high availability of the virtualized Cloud Server. The multi-component system, composed of the virtual machines and the virtual machine monitor, which are structurally dependent, has been reduced to the multiple two-component systems. The state-control-limit-based rejuvenation policy has been proposed and the stationary probability density of the two component system state has been derived. Furthermore, the stationary unavailability of the virtualized Cloud Server has been modeled. Numerical experiments have verified the correctness of the probability density function and the feasibility of the rejuvenation policy. The state-control-limit-based rejuvenation policy leads to lower unavailability of the virtualized Cloud Server in comparison with the lifetime-based rejuvenation policy.


References: 21

1.     J. Araujo, R. Matos, V. Alves, P. Maciel, F.V.D. Souza, R. Matias, and K.S. Trivedi, “Software Aging in the Eucalyptus Cloud Computing Infrastructure: Characterization and Rejuvenation,” Acm Journal on Emerging Technologies in Computing Systems, vol. 636, no. 8, pp. 1557-1564, 2014

2.     A. Avritzer and E.J. Weyuker, “Monitoring Smoothly Degrading Systems for Increased Dependability,” Empirical Software Engineering, vol. 2, no. 2, pp. 59-77, 1997

3.     P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, “Xen and the Art of Virtualization,” Acm Sigops Operating Systems Review, vol. 37, no. 5, pp. 164-177, 2003

4.     A. Bobbio, M. Sereno, and C. Anglano, “Fine Grained Software Degradation Models for Optimal Rejuvenation Policies,” Performance Evaluation, vol. 46, no. 1, pp. 45-62, 2001

5.     D. Cotroneo, R. Natella, R. Pietrantuono, and S. Russo, “Software Aging Analysis of the Linux Operating System,” 71-80, 2010

6.     R. Eberhart and J. Kennedy, “A New Optimizer Using Particle Swarm Theory,” in International Symposium on MICRO Machine and Human Science. pp. 39-43, 1995

7.     V.D.B. Frans and A.P. Engelbrecht, “A Cooperative Approach to Particle Swarm Optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225-239, 2004

8.     S. Garg, A.V. Moorsel, K. Vaidyanathan, and K.S. Trivedi, “A Methodology for Detection and Estimation of Software Aging,” in International Symposium on Software Reliability Engineering, 1998. Proceedings. pp. 283-292, 1998

9.     M. Grottke, L. Li, K. Vaidyanathan, and K.S. Trivedi, “Analysis of Software Aging in a Web Server,” Discussion Papers, vol. 55, no. 3, pp. 411 - 420, 2005

10.   M. Grottke and K.S. Trivedi, “Fighting Bugs: Remove, Retry, Replicate, and Rejuvenate,” Computer, vol. 40, no. 2, pp. 107-109, 2007

11.   Y. Huang, C. Kintala, N. Kolettis, and N.D. Fulton, “Software Rejuvenation: Analysis, Module and Applications,” in proceedings of 25 International Symposium, Fault-Tolerant Computing, FTCS-25. pp. 381-390, 1995

12.   J. Kennedy and R. Eberhart, “Particle Swarm Optimization.” Springer US, 2011

13.   K. Kourai and S. Chiba, “Fast Software Rejuvenation of Virtual Machine Monitors,” IEEE Transactions on Dependable & Secure Computing, vol. 8, no. 6, pp. 839-851, 2010

14.   J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar, “Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281-295, 2006

15.   F. Machida, S.K. Dong, J.S. Park, and K.S. Trivedi, “Toward Optimal Virtual Machine Placement and Rejuvenation Scheduling in a Virtualized Data Center,” in IEEE International Conference on Software Reliability Engineering Workshops, 2008. ISSRE Wksp. pp. 1-3, 2008

16.   F. Machida, S.K. Dong, and K.S. Trivedi, “Modeling and Analysis of Software Rejuvenation in a Server Virtualized System with Live VM Migration,” Performance Evaluation, vol. 70, no. 3, pp. 212-230, 2013

17.   F. Machida, J. Xiang, K. Tadano, and Y. Maeno, “Combined Server Rejuvenation in a Virtualized Data Center,” in International Conference on Ubiquitous Intelligence & Computing and International Conference on Autonomic & Trusted Computing. pp. 486-493, 2012

18.   S. Meyn and R.L. Tweedie, “Markov Chains and Stochastic Stability.” Springer-Verlag, 1993

19.   J.M.V. Noortwijk, “A Survey of the Application of Gamma Processes in Maintenance,” Reliability Engineering & System Safety, vol. 94, no. 1, pp. 2-21, 2009

20.   A. Rezaei and M. Sharifi, “Rejuvenating High Available Virtualized Systems,” in Ares '10 International Conference on Availability, Reliability, and Security. pp. 289-294, 2010

21.   J. Sahoo, S. Mohapatra, and R. Lath, “Virtualization: A Survey on Concepts, Taxonomy and Associated Security Issues,” in Second International Conference on Computer and Network Technology. pp. 222-226, 2010


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Download this file (IJPE-2018-03-08.pdf)IJPE-2018-03-08.pdf[State-Control-Limit-Based Rejuvenation Modeling of Virtualized Cloud Server]1718 Kb


Prev Next

Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

Kuo Chi, Jianshe Kang, Xinghui Zhang, and Zhiyuan Yang

Read more

Trust Authorization Monitoring Model in IoT

Ruizhong Du, Chong Liu, and Fanming Liu

Read more

Numerical Analysis of Ventilation for Ship E/R with CFD Method

Jianping Chen, Jie Xu, Litao Wang, Xinen Chen, and You Gong

Read more

Brushless DC Motor Control Strategy for Electric Vehicles

Wanmin Li, Xinyong Li, Yan Wang, Xianhao Zeng, and Yunzi Yang

Read more
This site uses encryption for transmitting your passwords.