Username   Password       Forgot your password?  Forgot your username? 


Reliability Simulation in Cloud Computing System

Volume 14, Number 9, September 2018, pp. 2015-2020
DOI: 10.23940/ijpe.18.09.p9.20152020

Sa Meng, Xiwei Qiu, Liang Luo, Han Xu, and Meilian Lei

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

(Submitted on May 23, 2018; Revised on July 16, 2018; Accepted on August 8, 2018)


With the large-scale increase of users, the reliability of the cloud system has become a challenging issue in the industry and academia. Many researchers have studied the reliability mechanism of cloud computing systems and proposed reliability awareness methods to achieve resource integration and improve system reliability. However, various hardware and software failures occur inevitably and cannot be accurately found and repaired in a timely manner. Moreover, since most of the studies cannot determine the background operation mechanism of the cloud system, this brings significant problems to the research of cloud computing reliability. To solve this problem, we first extract the key features that can be used to increase system reliability in cloud computing architectures. Secondly, we present an architecture framework for reliability simulation and analyze four types of common system failures: hardware failures, virtual machine failures, data inconsistency failures, and service timeout failures. Finally, experiments and verification based on a set of realistic configurations and operation runtimes are implemented as an extension of a well-known cloud simulation tool, CloudSim, to illustrate how these failures affect the reliability of cloud computing systems and how different resource scheduling algorithms handle these failures.


References: 17

              1. P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” (, accessed September 2011)
              2. E. Bauer and R. Adams, “Reliability and Availability of Cloud Computing,” John Wiley & Sons, 2012
              3. B. Yang, F. Tan, Y. S. Dai, and S. Guo, “Performance Evaluation of Cloud Service Considering Fault Recovery,” in Proceedings of IEEE International Conference on Cloud Computing, pp. 571-576, Beijing, China, December 2009
              4. M. Armbrust, A. Fox, R. Griffith, et al., “A View of Cloud Computing,” Communications of the ACM, Vol. 53, No. 4, pp. 50-58, April 2010
              5. B. P. Rimal, A. Jukan, D. Katsaros, et al., “Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud Approach,” Journal of Grid Computing, Vol. 9, No. 1, pp. 3-26, 2011
              6. R. Clarke, “User Requirements for Cloud Computing Architecture,” in Proceedings of 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 625-630, Melbourne, Australia, May 2010
              7. R. Buyya, C. S. Yeo, S. Venugopal, et al., “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility,” Future Generation computer systems, Vol. 25, No.6, pp. 599-616, 2009
              8. Y. S. Dai, B. Yang, J. Dongarra, and G. Zhang, “Cloud Service Reliability: Modeling and Analysis,” in Proceedings of 15th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 1-17, 2009
              9. P. Manuel, “A Trust Model of Cloud Computing based on Quality of Service,” Annals of Operations Research, Vol. 233, No. 1, pp. 281-292, 2015
              10. R. Matos, J. Dantas, J. Araujo, et al., “Redundant Eucalyptus Private Clouds: Availability Modeling and Sensitivity Analysis,” Journal of Grid Computing, Vol. 15, No. 1, pp. 1-22, 2017
              11. S. Bitam, A. Mellouk, and S. Zeadally, “VANET-cloud: A Generic Cloud Computing Model for Vehicular Ad Hoc Networks,” IEEE Wireless Communications, Vol. 22, No. 1, pp. 96-102, 2015
              12. S. Di, D. Kondo, and C. L. Wang, “Optimization of Composite Cloud Service Processing with Virtual Machines,” IEEE Transactions on Computers, Vol. 64, No. 6, pp. 1755-1768, 2015
              13. S. K. Garg, A. N. Toosi, S. K. Gopalaiyengar, et al., “SLA-based Virtual Machine Management for Heterogeneous Workloads in a Cloud Datacenter,” Journal of Network and Computer Applications, Vol. 45, pp. 108-120, 2014
              14. M. Qiu, Z. Ming, J. Li, K. Gai, and Z. Zong, “Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm,” IEEE Transactions on Computers, Vol. 64, No. 12, pp. 3528-3540, 2015
              15. Y. Zhang, S. Wang, and G. Ji, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and its Applications,” Mathematical Problems in Engineering, Vol. 2015, 2015
              16. L. Zuo, L. Shu, S. Dong, C. Zhu, and T. Hara, “A Multi-Objective Optimization Scheduling Method based on the Ant Colony Algorithm in Cloud Computing,” IEEE Access, Vol. 3, pp. 2687-2699, 2015
              17. R. N. Calheiros, R. Ranjan, A. Beloglazov, et al., “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms,” Software: Practice and experience, Vol. 41, No. 1, pp. 23-50, 2011


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

                          Download this file (09-IJPE-09-09.pdf)09-IJPE-09-09.pdf[Reliability Simulation in Cloud Computing System]567 Kb
                          This site uses encryption for transmitting your passwords.