The Rejuvenation Queue Construction and Strategy Research for Related Web Services

Abstract:

The problem of software aging widely exists in long-running software system, and the solution is software rejuvenation. Traditional software rejuvenation strategy has some deficiencies in solving the software aging problems of web services, for example, the higher failure rate and the poorer stability. Therefore, considering web services may have the correlation, at first we classified web service relationship and analyzed the relationship of web services by using the association mining algorithm; secondly we established and continuously fixed the aging model of web services by using the error back-propagation network to calculate the aging weights of web services. On this basis, based on the analysis of web services’ priority, call frequency and aging states, we calculated the rejuvenation cost, constructed the rejuvenation queue, and proposed an adaptive rejuvenation strategy of related web services. The experiment result showed that, compared with traditional regeneration strategy, this strategy improved the stability and dependability of web services.

• Periodical: Advanced Materials Research (Volumes 171-172)
• Pages: 688-691
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.171-172.688
• Online since: December 2010
• Authors: Jun Guo, Xin Ya Song, Yun Sheng Wang, Xian Li Li, Bin Zhang
• Keywords: Association Rule Mining, Error Back-Propagation Network, Rejuvenation Queue, Related Web Services, Software Aging

References

[1] C. Huang Kintala, N. Kolettis etc. Software Rejuvenation: Analysis, Module and Applications [C]. Proceedings of the 25th Symposium on Fault Tolerant Computer Systems, Pasadena, CA, 1995: 381—390.

DOI: 10.1109/ftcs.1995.466961

[2] V. Castelli, R.E. Harper, P. Heidelberger etc. Proactive Management of SoftwareAging[J], IBM JRD, 2001, 45(2): 311—332.

[3] S. Garg, A. Puliafito, M. Telek and K.S. Trivedi. Analysis of Software Rejuvenation using Markov Regenerative Stochastic Petri Nets[C]. In: int'l. Symp. On Software Reliability Engineering, ISSRE 1995, Oct, (1995).

DOI: 10.1109/issre.1995.497656

[4] S. Garg, A. van Moorsel, K. Vaidyanathan and K.S. Trivedi. A Methodology for Detection and Estimation of Software Aging[C]. In: int'l. symp. on Software Reliability Engineering, ISSRE 1998, Nov. (1998).

DOI: 10.1109/issre.1998.730892

[5] A. Artur and L Silva. Using Machine Learning for Non-Intrusive Modeling and Prediction of Software Aging[C]. In: Computers and Communications, 2008. ISCC 2008. IEEE.