Cluster Computing

, Volume 22, Supplement 5, pp 10425–10438 | Cite as

A novel method for adaptive fault tolerance during load balancing in cloud computing

  • T. TamilvizhiEmail author
  • B. Parvathavarthini


Cloud computing is the most promising technology that has been evolved to meet higher computation needs. But these high performance computing systems can have higher failure rates due to the larger number of servers and components filled with the intensive workloads. These failures in the sub-systems may lead to the unavailability of the systems for computation. Hence this issue of fault occurrences can be tolerated by adopting an effective and efficient fault tolerant technique. As cloud computing is more about on storage of data in a remote network, most of the faults occur due to system failure and network congestions. This proposed work introduces an innovative perspective on adopting a fault tolerant mechanism that shades the implementation of cloud server with cloud selection to avoid network congestion and health monitoring for fault detection with migration technique to adaptively handle the occurrence of faults. To effectively reduce the data unavailability, due to network traffic in the cloudlets of the cloud server.


Adaptive fault tolerance Proactive fault tolerance Reactive fault tolerance Elastic cloud balancing Virtual machine Migration 



The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. Special thanks go to Sathyabama Univesrsity for providing us with various resources and an unconditional support for carrying out this work.


  1. 1.
    Malik, S., Huet, F.: Adaptive fault tolerance in real time cloud computing. In: 2011 IEEE world congress, pp. 280-287. IEEE (2011)Google Scholar
  2. 2.
    Deng, J., Huang, S.C.H., Han, Y.S., Deng, J.H.: Fault-tolerant and reliable computation in cloud computing. In: IEEE workshop on web and pervasive security, pp. 160–1605 (2010)Google Scholar
  3. 3.
    Egwutuoha, I.P., Chen, S., Levy, D., Selic, B., Calvo, R.: A proactive fault tolerance approach to HPC in the cloud. In: International Conference on Cloud Computing, pp. 268–273. IEEE (2012)Google Scholar
  4. 4.
    Egwutuoha, I.P., Chen, S., Levy, D., Selic, B., Calvo, R.: Energy Efficient fault tolerance for HPC in the cloud. In: Cloud Computing International Conference, pp. 762–769. IEEE (2013)Google Scholar
  5. 5.
    Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)CrossRefGoogle Scholar
  6. 6.
    Bala, A., Chana, I., Tolerance- Challenges, F.: Techniques and implementation in cloud computing. Int. J. Comput. Sci. Issues 9(1), 288–293 (2012)Google Scholar
  7. 7.
    Eckart, B., Chen, X., He, X., Scott, S.L.: Failure prediction models for proactive fault tolerance within storage systems. In: International Symposium, pp. 1–8. IEEE (2008)Google Scholar
  8. 8.
    Engelmann, C., Vallee, G.R., Naughton, T., Scott, S.L.: Proactive fault tolerance using preemptive migration, PDP publisher, pp. 1–6 (2009)Google Scholar
  9. 9.
    Jhawar, R., Piuri, V., Santambrogio, M.: Fault tolerance management in cloud computing: a system-level perspective. IEEE Syst. J. 7(2), 288–296 (2013)CrossRefGoogle Scholar
  10. 10.
    Vallee, G., Charoenpornwattana, K., Engelmann, C., Tikotekar, A., Leangsuksun, C., Naughton, T., Scott, S.L.: A framework for the proactive fault tolerance. In: International conference on Availability Reliability Security, pp. 105–114. IEEE (2008)Google Scholar
  11. 11.
    Jhawar, R., Piuri, V., Santambrogio, M.: A comprehensive conceptual system-level approach to fault tolerance in cloud computing. In: 2012 IEEE International, pp. 1–5. IEEE (2012)Google Scholar
  12. 12.
    Teo, Y.M., Luong, B.L., Song, Y., Nam, T.: Cost-performance of fault tolerance in cloud computing. J. Sci. Technol. 49(4A), 61–73 (2011)Google Scholar
  13. 13.
    Derbal, Y.: A new fault-tolerance Framework for grid computing, multiagent and grid systems journal, vol. 2(2), IOS press, Amsterdam, pp. 115–133 (2006)Google Scholar
  14. 14.
    Chalermarrewong, T., Achalakul, T. and See, S.C.W.: The design of a fault management framework for cloud. In: International conference on electrical engineering/electronics, computer, telecommunications and information technology, pp. 1–4. IEEE (2012)Google Scholar
  15. 15.
    Zhao, W., Melliar-Smith, P.M., Moser, L.E.: Fault tolerance middleware for cloud computing. In: Proceedings IEEE International Conference on cloud computing, pp. 67–74 (2010)Google Scholar
  16. 16.
    Maheshwari, K., Lim, M., Wang, L., Birman, K., van Renesse, R.: Toward a reliable, secure and fault tolerant smart grid state estimation in the cloud. In: In Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES, pp. 1–6. IEEE (2013)Google Scholar
  17. 17.
    Jiang, L., Peng, X. and Xu, G.: Time and prediction based software Rejuvenation policy. In: In Information Technology and Computer Science (ITCS), 2010 Second International Conference on, pp. 114–117. IEEE (2010)Google Scholar
  18. 18.
    Randles, M., Lamb, D., Taleb-Bendiab, A.: A comparative study into distributed load balancing algorithms for cloud computing. In: 2010 IEEE 24th International Conference on, pp. 551–556. IEEE (2010)Google Scholar
  19. 19.
    Tamilvizhi, T., Varthini, P.B., Surendran, R.: An improved solution for resource management based on elastic cloud balancing and job shop scheduling. ARPN J. Eng. Appl. Sci. 10(18), 8205–8210 (2015)Google Scholar
  20. 20.
    Liu, Y., Gureya, D., Al-Shishtawy, A., Vlassov, V.: OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services. In: Cloud and Autonomic Computing (ICCAC), 2017 International Conference, pp. 50–59. IEEE (2017)Google Scholar
  21. 21.
    Tamilvizhi, T., Varthini, P.B.: Cessation of overloaded host by increase the inter-migration time in cloud data. J. Theor. Appl. Inf. Technol. 95(3), 654–660 (2017)Google Scholar

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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Faculty of ComputingSathyabama UniversityChennaiIndia
  2. 2.St. Joseph’s College of EngineeringChennaiIndia

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