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New Fuzzy-Based Fault Tolerance Evaluation Framework for Cloud Computing

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Abstract

Fault tolerance is one of the principal challenges in cloud computing. This capability has a trade off with other system features. Providing a fuzzy inference system to evaluate fault tolerance architectural capabilities in cloud computing systems is among the goals of this research. The proposed system is called CFTFIS. It evaluates the fault tolerance architecture and determines the level of fault tolerance based on fuzzy patterns. Three parameters of each architecture, i.e., policies, fault detection and fault recovery techniques, have been considered. Fault tolerance of the presented architecture and the increased percentage of the intended capability are obtained at five different fuzzy levels. If the vulnerability of a cloud computing system is identified, it is possible to make a correct and proper choice of the architecture that should be implemented in the system. Additionally, if the damage to a cloud computing system is defined based on the amount and type of the faults, it is possible to choose an architecture and implement it in the system based on the fault tolerance that each architecture provides.

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References

  1. Mell, P., Grance, T.: NIST Special Publication 800-145 The NIST Definition of Cloud Computing, Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899-8930, September (2011)

  2. Nazari Cheraghlou, M., Khadem-Zadeh, A., Haghparast, M.: A survey of fault tolerance architecture in cloud computing. J. Netw. Comput. Appl. 61, 81–92 (2016)

    Article  Google Scholar 

  3. Sudha Lakshmi, S.: Fault tolerance in cloud computing. ACICE. 4(1) (2013)

  4. Egwutuoha, I.P., Chen, S., Levy, D., Selic, B.: A fault tolerance framework for high performance computing in cloud. Cluster, Cloud and Grid Computing (CCGrid). In: 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 13–16 May, pp. 709–710. https://doi.org/10.1109/ccgrid.2012.80 (2012)

  5. Kumar Garga, S., Versteegb, S., Buyyaa, R.: A framework for ranking of cloud computing services. Future Gener. Comput. Syst. 29(4), 1012–1023 (2013)

    Article  Google Scholar 

  6. Suna, L., Ma, J., Zhanga, Y., Dong, H., Khadeer Hussainc, F.: Cloud-FuSeR: fuzzy ontology and MCDM based cloud service selection. Future Gener. Comput. Syst. 57, 42–55 (2016)

    Article  Google Scholar 

  7. Manvi, S.S., Krishna Shyam, G.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)

    Article  Google Scholar 

  8. Esposito, C., Ficco, M., Palmieri, F., Castiglione, A.: Smart cloud storage service selection based on fuzzy logic, theory of evidence and game theory. IEEE Trans. Comput. 65(8), 2348–2362 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  9. Rubio, J., Bouchachia, A.: MSAFIS: an evolving fuzzy inference system. Soft Comput. 21(9), 2357–2366 (2017)

    Article  Google Scholar 

  10. Mollaiy-Berneti, S.: Optimal design of adaptive neuro-fuzzy inference system using genetic algorithm for electricity demand forecasting in Iranian industry. Soft Comput. 20(12), 4897–4906 (2016)

    Article  Google Scholar 

  11. Lee, W., Jung, H., Yoon, J., Choi, S.: The statistical inferences of fuzzy regression based on bootstrap techniques. Soft. Comput. 19(4), 883–890 (2015)

    Article  MATH  Google Scholar 

  12. Kumar, S., Rana, D.S., Dimri, S.C.: Fault tolerance and load balancing algorithm in cloud computing: a survey. Int. J. Adv. Res. Comput. Commun. Eng. (IJARCCE) 4(7), 92–96 (2015)

    Google Scholar 

  13. Saikia, L.P., Devi, Y.L.: Fault tolerance techniques and algorithms in cloud computing. Int. J. Comput. Sci. Commun. Netw. 4(1), 01–08 (2014)

    Google Scholar 

  14. Amin, Z., Sethi, N., Singh, H.: Review on fault tolerance techniques in cloud computing. Int. J. Comput. Appl. 116, 11–17 (2015)

    Google Scholar 

  15. Hashem, I., Yaqoob, I., Anuar, N., Mokhtar, S., Ullah, Gani A., Khan, S.: The rise of big data on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)

    Article  Google Scholar 

  16. Inukollu, V., Arsi, S., Ravuri, S.: Security issues associated with big data in cloud computing. Int. J. Netw. Secur. Appl. (IJNSA) 6(3), 45–56 (2014)

    Google Scholar 

  17. Abadi Daniel, J.: Data management in the cloud: limitations and opportunities. Bull. IEEE Comput. Soc. Tech. Comm. Data Eng. 32(1), 3–12 (2009)

    Google Scholar 

  18. Purcell, B.M.: Big data using cloud computing. J. Technol. Res. 5(1), 1–8 (2014)

    MathSciNet  Google Scholar 

  19. Ahuja, S.P., Moore, B.: State of big data analysis in the cloud. Netw. Commun. Technol. 2(1), 62–68 (2013)

    Google Scholar 

  20. Zheng, Z., Zhou, T.C., Lyu, M. R., and King, I.: FT‐Cloud: A component ranking framework for fault‐tolerant cloud applications. In: 2010 IEEE 21st International Symposium on Software Reliability Engineering, pp. 398–407 (2010). https://doi.org/10.1109/issre.2010.28

  21. Bala, A., Chana, I.: Fault tolerance-challenges, techniques and implementation in cloud computing. Int. J. Comput. Sci. Issues (IJCSI) 9(1), 288–293 (2012)

    Google Scholar 

  22. Zhang, Y., Zheng, Z., Lyu, M. R.: BFTCloud: A Byzantine fault tolerance framework for voluntary-resource cloud computing. Cloud Comput. (CLOUD). In: 2011 IEEE International Conference on 4–9 July, pp. 444–451, (2011)

  23. Lim, J., Suh, T., Gil, J., Yu, H.: Scalable and leaderless Byzantine consensus in cloud computing environments. Inf. Syst. Front. 16(1), 19–34 (2014)

    Article  Google Scholar 

  24. Kaur, J., Kinger, S.: Analysis of different techniques used for fault tolerance. Int. J. Comput. Technol. (IJCSIT) 4(2), 737–741 (2013)

    Google Scholar 

  25. Jhawar, R., Piuri, V., and Santambrogio, M.: A comprehensive conceptual system level approach to fault tolerance in cloud computing. In: 2012 IEEE International on Systems Conference (SysCon), pp. 1–5 (2012)

  26. Zhao, W., Melliar P.M., Mose, L.E.: Fault tolerance middleware for cloud computing. In: 2010 IEEE 3rd International Conference on Cloud Computing, pp. 67–74 (2010)

  27. Machida, F., Andrade, E., Seong Kim, D., Trivedi, K.S.: Candy: component‐based availability modeling framework for cloud service management using Sys‐ML. In: 2011 30th IEEE International Symposium on Reliable Distributed Systems, pp. 209–218. (2011)

  28. Feng, Q., Han, J., Gao, Y., Meng, D.: Magi‐cube: High Reliability and Low Redundancy Storage Architecture for Cloud Computing. In: 2012 IEEE Seventh International Conference on Networking, Architecture, and Storage, pp. 89–93 (2012)

  29. Xiaoyi Lu, J., Yu, L., Zou, Y., and Zha, L.: Vega warden: a uniform user management system for cloud applications. In: 2010 Fifth IEEE International Conference on Networking, Architecture, and Storage, pp. 457–464 (2010)

  30. Jayadivya, S.K., Nirmala, J.S., Bhanu, M.S.S.: Fault tolerance workflow scheduling based on replication and resubmission of tasks in cloud computing. Int. J. Comput. Sci. Eng. (IJCSE) 4(6), 996–1006 (2012)

    Google Scholar 

  31. Arabnejad, H., Pahl, C., Estrada., G., Samir, A., Fowley, F.: A fuzzy load balancer for adaptive fault tolerance management in cloud platforms. In: IFIP International Federation for Information Processing (2017)

  32. Ahmad, W.H.O., Pervez, U., Qadir, J.: Reliability modeling and analysis of communication networks. J. Netw. Comput. Appl. 78(15), 191–215 (2017)

    Article  Google Scholar 

  33. Sun, D., Chang, G., Miao, C., Wang, X.: Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments. J. Supercomput. 66(1), 193–228 (2013)

    Article  Google Scholar 

  34. Bilal, K., Khalid, O., Malik, S.U.R., Shahid Khan, M.U., Khan, S.U., Zomaya, A.: Fault Tolerance in the Cloud. Encyclopedia of Cloud Computing. Wiley, New York (2016)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Mehdi Nazari Cheraghlou

  2. Iran Telecommunication Research Center (ITRC), Tehran, Iran

    Ahmad Khademzadeh

  3. Department of Computer Engineering,Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran

    Majid Haghparast

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  1. Mehdi Nazari Cheraghlou
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  2. Ahmad Khademzadeh
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  3. Majid Haghparast
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Correspondence to Ahmad Khademzadeh.

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Nazari Cheraghlou, M., Khademzadeh, A. & Haghparast, M. New Fuzzy-Based Fault Tolerance Evaluation Framework for Cloud Computing. J Netw Syst Manage 27, 930–948 (2019). https://doi.org/10.1007/s10922-019-09491-2

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