Biogeography-based optimization for optimal job scheduling in cloud computing

Applied Mathematics and Computation

Volumn 247, Issue , 2014, Pages 266-280

  Kim, Sung Soo ^a   Byeon, Ji Hwan ^b   Yu, Hong ^c   Liu, Hongbo ^d,e  

^a Kangwon National University   (South Korea)

^b Kaiem Co LTD   (South Korea)

^c DALIAN OCEAN UNIVERSITY   (China)

^d DALIAN MARITIME UNIVERSITY   (China)

^e UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

Biogeography based optimization (BBO); Cloud computing; Job scheduling; Swarm intelligence

Indexed keywords

BIOGEOGRAPHY-BASED OPTIMIZATIONS; BIOGEOGRAPHYBASED OPTIMIZATIONS (BBO); JOB SCHEDULING; SWARM INTELLIGENCE;

CLOUD COMPUTING;

EID: 84907549296     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2014.09.008     Document Type: Article

References (37)

1. Y. Wei, and M. Blake Service-oriented computing and cloud computing: challenges and opportunities IEEE Internet Comput. 14 6 2010 72 75
2. I. Foster, Y. Zhao, I. Raicu, and S. Lu Cloud computing and grid computing 360-degree compared Proceedings of the 2008 Grid Computing Environments Workshop 2008 IEEE 1 10
3. B. Xu, C. Zhao, E. Hu, and B. Hu Job scheduling algorithm based on Berger model in cloud environment Adv. Eng. Software 42 7 2011 419 425
4. S. Ostermann, R. Prodan, and T. Fahringer Extending grids with cloud resource management for scientific computing Proceedings of 10th IEEE/ACM International Conference on Grid Computing 2009 IEEE 42 49
5. L.F. Bittencourt, E.R. Madeira, and N.L. Da Fonseca Scheduling in hybrid clouds IEEE Commun. Mag. 50 9 2012 42 47
6. M. Garey, and D. Johnson Computers and Intractability: A Guide to the Theory of NP-Completeness 1979 W.H. Freeman & Co.
7. J. Wu, X. Xu, P. Zhang, and C. Liu A novel multi-agent reinforcement learning approach for job scheduling in grid computing Future Gener. Comput. Syst. 27 5 2011 430 439
8. S. Kim, J. Byeon, H. Liu, A. Abraham, and S. Mcloone Optimal job scheduling of grid computing using efficient binary artificial bee colony Soft Comput. 17 5 2013 867 882
9. M. Clerc Particle Swarm Optimization 2006 Wiley-ISTE
10. A. Yadav, and K. Deep Shrinking hypersphere based trajectory of particles in PSO Appl. Math. Comput. 220 2013 246 267
11. H. Liu, A. Abraham, and M. Clerc Chaotic dynamic characteristics in swarm intelligence Appl. Soft Comput. 7 3 2007 1019 1026
12. S. Krause, R. James, J.J. Faria, G.D. Ruxton, and J. Krause Swarm intelligence in humans: diversity can trump ability Animal Behaviour 81 5 2011 941 948
13. E. Cuevas, F. Sención-Echauri, D. Zaldivar, and M. Pérez-Cisneros Multi-circle detection on images using artificial bee colony (ABC) optimization Soft Comput. 16 2 2012 1 16
14. H. Liu, A. Abraham, and A. Hassanien Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm Future Gener. Comput. Syst. 26 8 2010 1336 1343
15. L. Babu, and P.V. Krishna Honey bee behavior inspired load balancing of tasks in cloud computing environments Appl. Soft Comput. 13 5 2013 2292 2303
16. D. Simon Biogeography-based optimization IEEE Trans. Evol. Comput. 12 6 2008 702 713
17. D. Simon, R. Rarick, M. Ergezer, and D. Du Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms Inf. Sci. 181 7 2011 1224 1248
18. P. Brucker Scheduling Algorithms 2007 Springer Verlag
19. M. Pinedo Scheduling: Theory, Algorithms, and Systems 2012 Springer Verlag
20. D.-M. Lei Minimizing makespan for scheduling stochastic job shop with random breakdown Appl. Math. Comput. 218 24 2012 11851 11858
21. S. Song, K. Hwang, and Y. Kwok Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling IEEE Trans. Comput. 55 6 2006 703
22. A. Tchernykh, U. Schwiegelshohn, R. Yahyapour, and N. Kuzjurin On-line hierarchical job scheduling on grids with admissible allocation J. Scheduling 13 5 2010 545 552
23. S. Chung, F. Chan, and H. Chan A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling Eng. Appl. Artif. Intell. 22 7 2009 1005 1014
24. S. Kirkpatrick, C. Gelatt, and M. Vecchi Optimization by simulated annealing Science 220 4598 1983 671
25. J. Kennedy, and R. Eberhart Particle swarm optimization Proceedings of IEEE Conference on Neural Networks 1995 IEEE 1942 1948
26. D. Karaboga, An idea based on honey bee swarm for numerical optimization, Techn. Rep. TR06, Erciyes Univ. Press, Erciyes.
27. E. Hou, N. Ansari, and H. Ren A genetic algorithm for multiprocessor scheduling IEEE Trans. Parallel Distrib. Syst. 5 2 1994 113 120
28. L. Wang, H.J. Siegel, V.P. Roychowdhury, and A.A. Maciejewski Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach J. Parallel Distrib. Comput. 47 1 1997 8 22
29. D. Simon, M. Ergezer, and D. Du Population distributions in biogeography-based optimization algorithms with elitism Proceedings of IEEE International Conference on Systems, Man and Cybernetics 2009 IEEE
30. H. Ma, and D. Simon Blended biogeography-based optimization for constrained optimization Eng. Appl. Artif. Intell. 24 3 2011 517 525
31. P. Roy, S. Ghoshal, and S. Thakur Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function Expert Syst. Appl. 37 12 2010 8221 8228
32. L.M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner A break in the clouds: towards a cloud definition ACM SIGCOMM Comput. Commun. Rev. 39 1 2008 50 55
33. P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield Xen and the art of virtualization ACM SIGOPS Operating Syst. Rev. 37 5 2003 164 177
34. H. Liu, A. Abraham, V. Snášel, and S. McLoone Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments Inf. Sci. 192 2012 228 243
35. H. Mann, and D. Whitney On a test of whether one of two random variables is stochastically larger than the other Ann. Math. Stat. 18 1 1947 50 60
36. R. Lahoz-Beltra, and C. Perales-Gravan A survey of nonparametric tests for the statistical analysis of evolutionary computational experiments Int. J. Inf. Theor. Appl. 17 1 2010 41 61
37. J. Derrac, S. García, D. Molina, and F. Herrera A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms Swarm Evol. Comput. 1 2011 3 18