Reliability and Performance Evaluation of Joint Redundancy and Inspection-Based Maintenance Strategy in Virtualized System

In virtualized system, both redundancy and inspection-based maintenance has been used to maintain reliability. Analysis is often conducted on a single strategy, and the overall impact of a joint mechanism has not been analyzed in detail. So, a method is presented to analyze the reliability and performance of virtualized system with the joint mechanism. The reliability and performance indicators are based on the Markov chain constructed from the state transition diagram for the joint mechanism. Sensitivity analysis is conducted to analyze the impact of system configuration parameters change. Empirical studies show the process of evaluation model and sensitivity analysis. Changing the value of redundancy and inspection rate value, the system reliability and performance could be calculated through the analysis method. The increase of redundancy leads to the increase of reliability and performance rate. Whereas, the increase in inspection rate could only improve the performance to an extent. The influence of both parameters declines rapidly as the value increases.

[1]  Kishor S. Trivedi.  Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .

[2]  David W. Coit,et al.  Maximization of System Reliability with a Choice of Redundancy Strategies , 2003 .

[3]  Daoud Aït-Kadi,et al.  Performance evaluation of multi-state degraded systems with minimal repairs and imperfect preventive maintenance , 2010, Reliab. Eng. Syst. Saf..

[4]  Ching-Hsien Hsu,et al.  On improvement of cloud virtual machine availability with virtualization fault tolerance mechanism , 2011, 2011 IEEE Third International Conference on Cloud Computing Technology and Science.

[5]  Hong-Zhong Huang,et al.  A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems , 2013, IEEE Transactions on Reliability.

[6]  Jin Zhang,et al.  Research about mobile AR system based on cloud computing , 2013, 2013 22nd Wireless and Optical Communication Conference.

[7]  Bran Selic,et al.  A survey of fault tolerance mechanisms and checkpoint/restart implementations for high performance computing systems , 2013, The Journal of Supercomputing.

[8]  Limin Xiao,et al.  Mvmotion: a metadata based virtual machine migration in cloud , 2013, Cluster Computing.

[9]  Zibin Zheng,et al.  Reliability-Based Design Optimization for Cloud Migration , 2014, IEEE Transactions on Services Computing.

[10]  Kishor S. Trivedi,et al.  Job completion time on a virtualized server with software rejuvenation , 2014, ACM J. Emerg. Technol. Comput. Syst..

[11]  Ju Zhang,et al.  An Animation Video Resource Conversion System Based on Supercomputers , 2014 .

[12]  Ying Li,et al.  Guaranteeing Fault-Tolerant Requirement Load Balancing Scheme Based on VM Migration , 2014, Comput. J..

[13]  Xin Xu,et al.  Exploring Data-Level Error Tolerance in High-Performance Solid-State Drives , 2015, IEEE Transactions on Reliability.

[14]  You Wu,et al.  Implementation of SVD Parallel Algorithm and its Application in Medical Industry , 2015 .

[15]  Jameela Al-Jaroodi,et al.  MidCloud: an agent‐based middleware for effective utilization of replicated Cloud services , 2015, Softw. Pract. Exp..

[16]  Inderveer Chana,et al.  Intelligent failure prediction models for scientific workflows , 2015, Expert Syst. Appl..