FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method

Cluster Computing

Volumn 18, Issue 2, 2015, Pages 829-844

  Shojafar, Mohammad ^a   Javanmardi, Saeed ^b   Abolfazli, Saeid ^c   Cordeschi, Nicola ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b Research and Education center   (Iran)

^c UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Cloud computing; Fuzzy theory; Genetic algorithm (GA); Job scheduling; Makespan; Mathematical optimization

Indexed keywords

ALGORITHMS; CLOUD COMPUTING; COMPUTATION THEORY; COMPUTATIONAL COMPLEXITY; COSTS; HEURISTIC METHODS; NETWORK MANAGEMENT; OPTIMIZATION; SCHEDULING ALGORITHMS;

FUZZY THEORY; JOB SCHEDULING; JOB SCHEDULING ALGORITHMS; MAKESPAN; MATHEMATICAL OPTIMIZATIONS; META-HEURISTIC APPROACH; OPTIMAL LOAD BALANCING; STANDARD GENETIC ALGORITHM;

SCHEDULING;

EID: 84939120748     PISSN: 13867857     EISSN: 15737543     Source Type: Journal    
DOI: 10.1007/s10586-014-0420-x     Document Type: Article

References (38)

1. Mezmaz, M., et al.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71(11), 1497–1508 (2011)
2. Armbrust, M., et al.: A view of cloud computing. Commun ACM 53(4), 50–58 (2010)
3. Dikaiakos, M.D., Pallis, G., Katsaros, D., Mehra, P., Vakali, A.: Cloud computing: distributed Internet computing for IT and scientific research. IEEE Internet Comput. 13(5), 10–13 (2009)
4. Rimal, B. P., Eunmi, C., Lumb, I. A.: Taxonomy and Survey of Cloud Computing Systems. In: Fifth International Joint Conference on INC, IMS and IDC, Seoul, 2009, pp. 44–51.
5. Li, Q., Yike, G.: Optimization of resource scheduling in cloud computing. IEEE SYNASC, Timisoara (2010)
6. Cordeschi, N., Shojafar, M., Baccarelli, E.: Energy-saving self-configuring networked data centers. Computer Networks 57(17), 3479–3491 (2013)
7. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2–3), 95–99 (1988)
8. Vas, P.: Artificial-intelligence-based electrical machines and drives: application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques. Oxford University Press, Oxford (1999)
9. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. In: IEEE Transactions on Systems, Man and Cybernetics, SMC- 15(1)1 116–132 (1985).
10. Javanmardi, S., Shojafar, M., Amendola, D., Cordeschi, N., Liu, H., Abraham, A.: Hybrid job scheduling algorithm for cloud computing environment. Adv. Intell. Syst. Comput. 303, 43–52 (2014)
11. Javanmardi, S., Shojafar, M., Shariatmadari, Sh, Abawajy, J.H., Singhal, M.: PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture. The Journal of Supercomputing 69(2), 955–975 (2014)
12. Randles, M., Lamb, D., Taleb-Bendiab, A.: A comparative study into distributed load balancing algorithms for cloud computing. In: IEEE Advanced Information Networking and Applications Workshops (WAINA), pp. 551–556. WA, Perth (2010)
13. X. Baowen, G. Yu, Ch. Zhenqiang, K. R. P. H. Leung, Parallel genetic algorithms with schema migration. In: Computer Software and Applications Conference (COMPSAC), pp. 879–884 (2002).
14. Zhongni, Z., Wang, R., Hai, Z., Xuejie, Z.: An approach for cloud resource scheduling based on Parallel Genetic Algorithm. In: ICCRD IEEE Shanghai, China, 2, 444–447 (2011)
15. Hu, J., Jianhua, G., Guofei, S., Tianhai, Z.: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment. In: IEEE PAAP. pp. 89–96. Dalian, China (2010)
16. Singh, R.M., Sendhil Kumar, K.S., Jaisankar, N.: Comparison of probabilistic optimization algorithms for resource scheduling in cloud computing environment. Int. J. Eng. Technol. 5(2), 1419–1427 (2013)
17. Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. In: Proceedings of the Institution of Electrical Engineer, IET Digital Library 121(12), 1585–1588 (1974)
18. Vignesh, V., Sendhil, K.S., Jaisankar, N.: Resource Management and Scheduling in Cloud Environment. Int. J. Sci. Res. Publ. 3(6), 1–6 (2013)
19. Beloglazov, A., Buyya, R.: Energy Efficient Resource Management in Virtualized Cloud Data Centers. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGRID), Melbourne, Australia, pp. 826–831 (2010).
20. Chen, S., Wu, J., Lu, Z.: A Cloud Computing Resource Scheduling Policy Based on Genetic Algorithm with Multiple Fitness. In: IEEE 12th International Conference on Computer and Information Technology, Chengdu, pp. 177–184 (2012)
21. Sh. Sawant, A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment, Msc Thesis, (2011)
22. Kaur, S., Verma, A.: An efficient approach to genetic algorithm for job scheduling in cloud computing environment. Int. J. Info. Technol. Comput. Sci. 4(10), 74–79 (2012)
23. Kumar, V.V., Dinesh, K.: Job scheduling using fuzzy neural network algorithm in cloud environment. Int. J. Man Mach. Interface 2(1), 1–6 (2012)
24. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Pract. Experience 41(1), 23–50 (2011)
25. Abirami, S.P., Ramanathan, Sh: Linear scheduling strategy for resource allocation in cloud environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA) 2(1), 9–17 (2012)
26. Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud Computing and Grid Computing 360-Degree Compared, Grid Computing Environments Workshop (GCE ’08), pp. 1–10. Austin, TX, (2008)
27. 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)
28. Buyya, R., Broberg, J., Goscinski, A.M.: Cloud computing: principles and paradigms. John Wiley and Sons, New York (2011)
29. Joyent official Site. http://www.joyent.com/. Accessed 2014
30. Microsoft’s Windows Azure official Site. https://azure.microsoft.com/. Accessed 2014
31. Rackspace official Site. www.rackspace.com/. Accessed 2014
32. Dadgar, M., Hosseini, M.V., Merati, A.A., Sarkheyli, A.: Comparison of Mamdani and Sugeno fuzzy inference system in prediction of residual frieze effect of frieze carpet yarns. Tekstilna Industrija 61(2), 16–25 (2013)
33. Javanmardi, S., Shariatmadari, Sh, Mosleh, M.: A novel decentralized fuzzy based approach for grid resource discovery. Int. J. Innov. Comput. 3(1), 23–32 (2013)
34. Javanmardi, S., Shojafar, M., Shariatmadari, Sh, Ahrabi, S.S.: FR TRUST: a fuzzy reputation based model for Trust management in semantic P2P grids. Int. J. Grid Utility Comput. 6(1), 57–66 (2015)
35. Medhat, A.T., Ashraf, E.S., Arabi, E.K., Fawzy, A.T.: Hybrid job scheduling algorithm for cloud computing environment. Adv. Intell. Syst. Comput. 303, 43-52 (2014), Atlantis Press, pp. 169–172 (2013)
36. Du, D.-Z., Ko, K.-I.: Theory of computational complexity. John Wiley and Sons, New York (2011)
37. Ephzibah, E.P.: Time complexity analysis of genetic-fuzzy system for disease diagnosis, Advanced Computing, 2(4), 23–31 (2011)
38. Lotfi Zadeh, A.: A computational approach to fuzzy quantifiers in natural languages. Computers Math. Appl. 9(1), 149–184 (1983)