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A study on fuzzy logic based cloud computing

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Abstract

Cloud computing is now being deployed in real world to satisfy several users’ requirements for computation. In the point of management, there are several important considerations such as availability, reliability, resource utilization, and throughput in cloud computing. However, since these performance metrics are affected by the many uncorrelated parameters, it is very hard task to derive new model which takes into them account together. Even though there are many feasible models, fuzzy logic can be the most suitable one in the view of depth, popularity and applicability in many other research areas. However, as far as the authors know, there is only one short survey paper which focuses on introducing research challenges without detail discussion of each mechanism. Based on this deficiency, in this paper, we present the state-of-the-art approaches and their important features in fuzzy logic based cloud computing. First, we present overview of cloud computing and categorization for the current research works. Second, we also provide some of the key techniques presented in the recent literature and provide a summary of related research works. Finally, we suggest potential directions for future research in the field.

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References

  1. Wahiduzaman, M., Gani, A., Anur, N., Shiraz, M., Haque, M., Haque, I.: Cloud service selection using multicriteria decision analysis. Sci. World J. 2014, 1–10 (2014)

    Google Scholar 

  2. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  3. Zadeh, L.A.: The role of fuzzy logic in modeling, identification and control. Model. Identif. Control 15(3), 191–203 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  4. Prasath, V., Nithya Bharathan, N., Lakshmi, N.: Fuzzy logic in cloud computing. Int. J. Eng. Res. Technol. 2(3), 1–5 (2013)

    Google Scholar 

  5. Buyya, R., Broberg, J., Goscinski, A.M.: Cloud Computing Principle and Paradigms. Wiley, New Jersey (2011)

    Book  Google Scholar 

  6. Freed, D., Agrawal, R., John, S., Walker, J.J.: Cloud forensics challenges from a service model standpoint: Iaas, Paas and Saas,” In: Proceedings of ACM International Conference on Management of Computational and Collective Intelligence in Digital EcoSystem, pp. 148–155 (2015)

  7. Sotomayor, B., Montero, R.S., Llorente, I.M., Foster, I.: Virtual machine infrastructure management in private and hybrid clouds. IEEE Internet Comput. 13(5), 14–22 (2009)

    Article  Google Scholar 

  8. Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34(1), 1–11 (2011)

    Article  Google Scholar 

  9. Hayes, B.: Cloud computing. Commun. ACM 51(7), 9–11 (2008)

    Article  Google Scholar 

  10. Youseff, L., Butrico, M., Silva, D.: Towards a unified ontology of cloud computing. In: Proceedings of Grid Computing Environments Workshop, pp. 1–10 (2008)

  11. Xue, J., Li, L., Zhao, S., Jiao, L.: A study of task scheduling based on differential evolution algorithm in cloud computing. In: Proceedings of IEEE conference on Computational Intelligence and Communication Networks, pp. 637–640 (2014)

  12. Alla, H., Alla, S.B., Ezzati, A., Mouhsen, A.: A novel architecture with dynamic queues based on fuzzy logic and particle swarm optimization algorithm for task scheduling in cloud computing. In: El-Azouzi, R., Menasche, D., Sabir, E., De Pellegrini, F., Benjillali, M. (eds.) Advances in Ubiquitous Networking, vol. 397, pp. 205–217. Springer, Singapore (2016)

    Chapter  Google Scholar 

  13. Pandey, S., Wu, L., Guru, S., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Proceedings of IEEE International Conference on Advanced information networking and applications, pp. 400–407 (2010)

  14. Xu, B., Wang, K., Wang, Y.: An improved artificial bee colony algorithm for cloud computing service composition. In: Proceedings of IEEE International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, pp. 310–317 (2015)

  15. Lee, B., Oh, K., Park, H., Kim, U., Youn, H.: Resource reallocation of virtual machine in cloud computing with MCDM algorithm. In: Proceedings of IEEE International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp. 470–477 (2014)

  16. Qu, L., Wang, Y., Orgun, M.A.: Cloud service selection based on the aggregation of user feedback and quantitative performance assessment. In: Proceedings of IEEE International Conference on Services Computing, pp. 152–159 (2013)

  17. Junior, R., Romlim, T.: A multi-criteria approach for assessing cloud deployment options based on non-functional requirements. In: Proceedings of the Annual ACM Symposium on Applied Computing, pp. 1383–1389 (2015)

  18. Opricovic, S., Tzeng, G.H.: Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2), 445–455 (2004)

    Article  MATH  Google Scholar 

  19. Fan, C., An, C.: Employing the grey relational analysis to identify and evaluate cloud computing risk. Int. J. Recent Res. Appl. Stud. 15(1), 1–11 (2013)

    Google Scholar 

  20. Zhao, J., Bose, B.: Evaluation of membership function for fuzzy logic controlled induction motor drive. Proc. IEEE Annu. Conf. Ind. Electron. Soc. 4, 229–234 (2002)

    Google Scholar 

  21. Nine, M., Azad, M., Abdullah, S., Rahman, R.M.: Fuzzy logic based dynamic load balancing in virtualized data centers. In: Proceedings of IEEE International Conference on Fuzzy Systems (2013)

  22. Priya, V., Babu, C.: Moving average fuzzy resource scheduling for virtualized cloud data services. Comput. Stand. Interfaces 50, 251–257 (2017)

    Article  Google Scholar 

  23. Kong, X., Lin, C., Jiang, Y., Tan, W., Chu, X.: Efficient dynamic task scheduling in virtualized data center with fuzzy prediction. J. Netw. Comput. Appl. 34, 1068–1077 (2011)

    Article  Google Scholar 

  24. Kumar, V.V., Dinesh, K.: Job scheduling using fuzzy neural network algorithm in cloud environment. Bonfring Int. J. Man Mach. Interface 2(1), 1–6 (2012)

    Article  Google Scholar 

  25. Grandhi, S., Wibowo, S.: Performance evaluation of cloud computing providing using fuzzy multiattribute group decision making model. In: Proceedings of IEEE International Conference on Fuzzy System and Knowledge Discovery, pp. 130–135 (2015)

  26. Xu, J., Zhao, M., Fortes, J., Carpenter, R., Yousif, M.: On the use of fuzzy modeling in virtual data center management. In: Proceedings of IEEE International Conference on Autonomic Computing (2007)

  27. Baliyan, N., Kumar, S.: A hierarchical fuzzy system for quality assessment of semantic web application as a service. ACM SIGSOFT Softw. Eng. Notes 41(1), 1–7 (2016)

    Article  Google Scholar 

  28. Barua, A., Mudunuri, L., Kosheleva, O.: Why trapezoidal and triangular membership function work so well: towards a theoretical explanation. J. Uncertain Syst. 8(3), 164–168 (2014)

    Google Scholar 

  29. Sethi, S., Sahu, A., Jena, S.K.: Efficient load balancing in cloud computing using fuzzy logic. IOSR J. Eng. 2(2), 65–71 (2012)

    Article  Google Scholar 

  30. Mehranzadeh, A., Hashemi, S.M.: A novel- scheduling algorithm for cloud computing based on fuzzy logic. Int. J. Appl. Inf. Syst. 5(7), 28–31 (2013)

    Google Scholar 

  31. Minarolli, D., Freisleben, B.: Virtual machine resource allocation in cloud computing via multi-agent fuzzy control. In: Proceedings of IEEE International Conference on Cloud and Green Computing, pp. 188–194 (2013)

  32. Chen, Z., Zhu, Y., Di, Y., Feng, S.: A dynamic resource scheduling method based on fuzzy control theory in cloud environment. J. Control Sci. Eng. 2015, 10 (2015)

    Article  MATH  Google Scholar 

  33. Albano, L., Anglano, C., Canonico, M., Guazzone, M.: Fuzzy—Q&E: achievement QoS guarantees and energy saving for cloud application with fuzzy control. In: Proceedings of IEEE International Conference on Cloud and Green computing, pp. 159166 (2013)

  34. Toosi, A.N., Buyya, R.: A fuzzy logic-based controller for cost and energy efficiency load balancing in geo- distributed data centers. In: Proceedings of IEEE/ACM 8th International Conference on Utility and Cloud Computing, pp. 186–194 (2015)

  35. Ramezani, F., Naderpour, M., Lu, J.: A multi-objective optimization model for virtual machine mapping in cloud data center. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1259–1265 (2016)

  36. 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 

  37. Saripalli, P., Pingali, G.: MADMAC: multiple attribute decision for adoption of clouds. In: Proceedings of IEEE International Conference on Cloud Computing, pp. 316–323 (2011)

  38. Qu, C., Buyya, R.: A cloud trust evaluation system using hierarchical fuzzy inference system for service selection. In: Proceedings of IEEE International Conference on Advanced Information Networking and Application, pp. 850–857 (2014)

  39. Hosseini, S.R., Adabi, S., Tavoli, R.: A near optimal approach in choosing the appropriate physical machine for live virtual machine in cloud computing. J. Adv. Comput. Eng. Technol. 1(3), 23–32 (2015)

    Google Scholar 

  40. Lo, C., Tsai, C.F., Chao, K.M.: Service selection based on fuzzy TOPSIS method. In: Proceedings of IEEE International Conference on Advanced Information Networking and Application Workshops, pp. 367–372 (2010)

  41. Wu, H., Wang, Q., Wolter, K.: Methods of clouds-path selection for offloading in mobile cloud computing systems. In: Proceedings of IEEE International Conference on Cloud Computing Technology and Science, pp. 443–448 (2012)

  42. Su, C.H., Tzeng, G.H., Tseng, H.L.: Improving cloud computing service in fuzzy environment-combining fuzzy DANP and Fuzzy VIKOR with a new Hybrid FMCDM model. In: Proceedings of IEEE International Conference on Fuzzy Theory and Its Application, pp. 30–35 (2012)

  43. Alabool, H.M., Mahmood, A.K.: Trust-based service selection in public cloud computing using fuzzy modified VIKOR method. Aust. J. Basic Appl. Sci. 7(9), 211–220 (2013)

    Google Scholar 

  44. Tajvidi, M., Ranjan, R., Kolodziej , J., Wang, L.: Fuzzy cloud service selection framework. In: Proceedings of IEEE International Conference on Cloud Networking, pp. 443–448 (2014)

  45. Singla, C., Kaushal, S.: Cloud path selection using fuzzy analytic hierarchy process for offloading in mobile cloud computing. In: Proceedings of IEEE International Conference on Recent Advances in Engineering and Computational Sciences (2015)

  46. Tarighi, M., Motamedi, S.A., Sharifian, S.: A new model of virtual machine migration in virtualized cluster server based on fuzzy decision making. J. Telecommun. 1(1), 40–51 (2010)

    Google Scholar 

  47. Jaiganesh, M, Antony Kumar, A.V.: B3: fuzzy based data center load optimization in cloud computing. Math. Prob. Eng. (2013)

  48. Xiaojun, W., Yun, W., Zhe, H., D. Juan, D.: The research on resource scheduling based on fuzzy clustering in cloud computing. In: Proceedings of IEEE International Conference on Intelligent Computation Technology and Automation, pp. 1025–1028 (2015)

  49. Su, T., Wang, S., Vu, H., Ku, D., J. Huang, J.: An application of fuzzy theory to the power monitoring system in cloud environments, In: Proceedings of IEEE International Symposium on Computer, Consumer and Control, pp. 350–354 (2016)

  50. Mukherjee, K., Sahoo, G.: Mathematical model of cloud computing framework using fuzzy bee colony optimization technique. In: Proceedings of IEEE International conference on Advances in computer, control and Telecommunication Technologies, pp. 664–668 (2009)

  51. Singhal, U., Jain, S.: A new fuzzy logic and GSO based load balancing mechanism for public cloud. Int. J. Grid Distrib. Comput. 7(5), 97–110 (2014)

    Article  Google Scholar 

  52. Ramezani, F., Naderpour, M., J. Lu, J.: Handling uncertainty in cloud resource management using fuzzy bayesian network. In: Proceedings of IEEE International Conference on Fuzzy Systems (2015)

  53. Monil, M.A.H., Rahman, R.M.: VM consolidation approach based on heuristics, fuzzy logic, and migration control. J. Cloud Comput. 5(1), 1–8 (2016)

    Article  Google Scholar 

  54. Ooi, B.Y., Chan, H.Y., Cheah, Y.N.: Resource selection using fuzzy logic for dynamic service placement and replication. Proceedings of IEEE Trends and Development in Converging Technology towards 2020, pp. 128–132 (2011)

  55. Jamshidi, P., Ahmad, A., Pahl, C.: Autonomic resource provisioning for cloud-based software. In: Proceedings of ACM International Symposium on Software Engineering for Adaptive and Self Managing Systems, pp. 95–104 (2014)

  56. Mon, M.A.H., Rahman, R.M.: Fuzzy logic based energy aware vm consolidation. In: Proceedings of International Conference on Internet and Distributed Computing System, pp. 31–38 (2015)

  57. AliPour, M.M., Derakhshi, M.R.F.: Two level fuzzy approach for dynamic load balancing in the cloud computing. J. Electron. Syst. 6(1), 17–31 (2016)

  58. Piegat, A.: Fuzzy Modeling and Control. Springer, Berlin (2013)

    MATH  Google Scholar 

  59. Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, New York (2009)

    Google Scholar 

  60. Ahmad, J., Siyal, M.Y., Najam, S., Najam, Z.: Fuzzy Logic Based Power Efficient Real Time Multi-core Systems. Springer, Berlin (2016)

    Google Scholar 

  61. Mya, S., Thein, N.L.: A resource pool management model using fuuzzy logic decision making. Int. J. Comput. Appl. 29(10), 24–31 (2011)

    Google Scholar 

  62. Perumal, B., Murugaiyan, A.: A firefly colony and its fuzzy approach for server consolidation and virtual machine placement in cloud datacenters. Adv. Fuzzy Syst. 2016, 1–15 (2016). doi:10.1155/2016/6734161

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant no. NRF-2015R1D1A3A01019680) and “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20174030201440).

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Authors and Affiliations

  1. Department of Informatics, Gyeongsang National University, Jinju, 52828, Korea

    Bashir Hayat & Kyong Hoon Kim

  2. Department of Computer Science and Engineering, Chungnam National University, Daejeon, 34134, Korea

    Ki-Il Kim

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  1. Bashir Hayat
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Hayat, B., Kim, K.H. & Kim, KI. A study on fuzzy logic based cloud computing. Cluster Comput 21, 589–603 (2018). https://doi.org/10.1007/s10586-017-0953-x

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