Modeling and optimizing periodically inspected software rejuvenation policy based on geometric sequences

Software aging is characterized by an increasing failure rate, progressive performance degradation and even a sudden crash in a long-running software system. Software rejuvenation is an effective method to counteract software aging. A periodically inspected rejuvenation policy for software systems is studied. The consecutive inspection intervals are assumed to be a decreasing geometric sequence, and upon the inspection times of software system and its failure features, software rejuvenation or system recovery is performed. The system availability function and cost rate function are obtained, and the optimal inspection time and rejuvenation interval are both derived to maximize system availability and minimize cost rate. Then, boundary conditions of the optimal rejuvenation policy are deduced. Finally, the numeric experiment result shows the effectiveness of the proposed policy. Further compared with the existing software rejuvenation policy, the new policy has higher system availability.

[1]  Ann T. Tai,et al.  On-Board Preventive Maintenance: A Design-Oriented Analytic Study for Long-Life Applications , 1999, Perform. Evaluation.

[2]  E Marshall,et al.  Fatal error: how patriot overlooked a scud. , 1992, Science.

[3]  Dong Seong Kim,et al.  Modeling and analysis of software rejuvenation in a server virtualized system with live VM migration , 2013, Perform. Evaluation.

[4]  Kai-Yuan Cai,et al.  Multi-granularity Software Rejuvenation Policy Based on Continuous Time Markov Chain , 2011, 2011 IEEE Third International Workshop on Software Aging and Rejuvenation.

[5]  Kishor S. Trivedi,et al.  Software aging in the eucalyptus cloud computing infrastructure , 2014, ACM J. Emerg. Technol. Comput. Syst..

[6]  Elaine J. Weyuker,et al.  Monitoring Smoothly Degrading Systems for Increased Dependability , 2004, Empirical Software Engineering.

[7]  Daniel P. Siewiorek,et al.  High-availability computer systems , 1991, Computer.

[8]  Elaine J. Weyuker,et al.  Methods and opportunities for rejuvenation in aging distributed software systems , 2010, J. Syst. Softw..

[9]  Kishor S. Trivedi,et al.  Proactive management of software aging , 2001, IBM J. Res. Dev..

[10]  Helmut Fischer,et al.  A Software Reliability Model Based on a Geometric Sequence of Failure Rates , 2006, Ada-Europe.

[11]  P. M. Nagel,et al.  Software reliability: Repetitive run experimentation and modeling , 1982 .

[12]  Kishor S. Trivedi,et al.  Analysis of inspection-based preventive maintenance in operational software systems , 2002, 21st IEEE Symposium on Reliable Distributed Systems, 2002. Proceedings..

[13]  Kishor S. Trivedi,et al.  Analysis of Software Aging in a Web Server , 2006, IEEE Transactions on Reliability.

[14]  Domenico Cotroneo,et al.  A survey of software aging and rejuvenation studies , 2014, ACM J. Emerg. Technol. Comput. Syst..

[15]  Robert S. Hanmer,et al.  Software rejuvenation , 2010, PLOP '10.

[16]  Kishor S. Trivedi,et al.  Performance Assurance via Software Rejuvenation: Monitoring, Statistics and Algorithms , 2006, International Conference on Dependable Systems and Networks (DSN'06).

[17]  Domenico Cotroneo,et al.  A measurement‐based ageing analysis of the JVM , 2013, Softw. Test. Verification Reliab..

[18]  Kishor S. Trivedi,et al.  Analysis and implementation of software rejuvenation in cluster systems , 2001, SIGMETRICS '01.

[19]  Jim Gray,et al.  Why Do Computers Stop and What Can Be Done About It? , 1986, Symposium on Reliability in Distributed Software and Database Systems.

[20]  Jordi Torres,et al.  J2EE instrumentation for software aging root cause application component determination with AspectJ , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[21]  Paulo Romero Martins Maciel,et al.  Experimental evaluation of software aging effects in the eucalyptus elastic block storage , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[22]  Yennun Huang,et al.  Software rejuvenation: analysis, module and applications , 1995, Twenty-Fifth International Symposium on Fault-Tolerant Computing. Digest of Papers.

[23]  Antoine Grall,et al.  Continuous-time predictive-maintenance scheduling for a deteriorating system , 2002, IEEE Trans. Reliab..

[24]  Robert B. Randall,et al.  An inspection model with minimal and major maintenance for a system with deterioration and Poisson failures , 2000, IEEE Trans. Reliab..

[25]  Wei Xie,et al.  Analysis of a two-level software rejuvenation policy , 2005, Reliab. Eng. Syst. Saf..

[26]  Mark Sullivan,et al.  Software defects and their impact on system availability-a study of field failures in operating systems , 1991, [1991] Digest of Papers. Fault-Tolerant Computing: The Twenty-First International Symposium.

[27]  Tadashi Dohi,et al.  Dynamic software rejuvenation policies in a transaction-based system under Markovian arrival processes , 2013, Perform. Evaluation.

[28]  Kishor S. Trivedi,et al.  Accelerated Degradation Tests Applied to Software Aging Experiments , 2010, IEEE Transactions on Reliability.

[29]  Kenny C. Gross,et al.  Advanced pattern recognition for detection of complex software aging phenomena in online transaction processing servers , 2002, Proceedings International Conference on Dependable Systems and Networks.

[30]  Kishor S. Trivedi,et al.  A comparative experimental study of software rejuvenation overhead , 2013, Perform. Evaluation.

[31]  Tadashi Dohi,et al.  Discrete-time cost analysis for a telecommunication billing application with rejuvenation , 2006, Comput. Math. Appl..

[32]  Kishor S. Trivedi,et al.  Analysis of Preventive Maintenance in Transactions Based Software Systems , 1998, IEEE Trans. Computers.