Self-adaptive learning pso-based deadline constrained task scheduling for hybrid iaas cloud

IEEE Transactions on Automation Science and Engineering

Volumn 11, Issue 2, 2014, Pages 564-573

  Zuo, Xingquan ^a   Zhang, Guoxiang ^b   Tan, Wei ^c  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b INSTITUTE OF MICROELECTRONICS   (China)

^c IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Hybrid cloud; infrastructure as a service (IaaS) cloud; particle swarm optimization (PSO); task scheduling

Indexed keywords

ECONOMICS; INTEGER PROGRAMMING; MULTITASKING; PARTICLE SWARM OPTIMIZATION (PSO); PROFITABILITY; QUALITY OF SERVICE; SCHEDULING; SCHEDULING ALGORITHMS;

CLOUD FEDERATIONS; COMPUTATION TIME; COMPUTE RESOURCES; CONSTRAINED TASKS; HYBRID CLOUDS; SELF-ADAPTIVE LEARNING; TASK-SCHEDULING; UPDATING STRATEGY;

INFRASTRUCTURE AS A SERVICE (IAAS);

EID: 84898820315     PISSN: 15455955     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASE.2013.2272758     Document Type: Article

References (39)

1. S. Bhardwaj, L. Jain, and S. Jain, "Cloud computing: A study of infrastructureas a service (IaaS)," Int. J. Eng. Inf. Technol., vol. 2, no. 1,pp. 60-63, 2010.
2. R. Buyya, C. S. Yeo, S. Venugopal, and J. Broberg, "Cloud computingand emerging IT platforms: Vision, hype, and reality for deliveringcomputing as the 5th utility," Future Generation Comput. Syst., vol.25, no. 6, pp. 599-616, 2009.
3. A. N. Toosi, R. N. Calheiros, P. K. Thulasiram, and R. Buyya, "Resourceprovisioning policies to increase IaaS provider's profit in a federatedcloud environment," in Proc. IEEE Int. Conf. High Perform.Comput. Commun., Banff, Canada, 2011, pp. 279-287.
4. D. Breitgand, A. Maraschini, and J. Tordsson, Policy-Driven ServicePlacement Optimization in Federated Cloud, IBM Research Report,2011.
5. S. Ortiz, "The problem with cloud-computing standardization," Computer,vol. 44, no. 7, pp. 13-16, 2011.
6. H. Liu and D. Orban, "GridBatch: Cloud computing for large-scaledata-intensive batch applications," in Proc. IEEE Int. Symp. ClusterComput. Grid, 2008, pp. 295-305.
7. J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc.IEEE Conf. Neural Netw., Piscataway, NJ, 1995, pp. 1942-1948.
8. Y. Shi and R. Eberhart, "Empirical study of particle swarm optimization,"in Proc. IEEE Congr. Evol. Comput., Washington, DC, USA,1999, pp. 1945-1950.
9. R. Xiao and J. Yu, "A newly self-adaptive strategy for the PSO," inProc. Int. Conf. Natural Comput., 2008, pp. 396-400.
10. B. Niu, Y. Zhu, K. Hu, S. Li, and X. He, "A novel particle swarmoptimizer using optimal for foraging theory," in Proc. Comput. Intell.Bioinformat., Kunming, China, 2006, pp. 61-71.
11. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, "Self- organizinghierarchical particle swarm optimizer with time-varying accelerationcoefficients," IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240-255,Jun. 2004.
12. J. Kennedy, "Small worlds and mega-minds: Effects of neighborhoodtopology on particle swarm performance," in Proc. IEEE Congr. Evol.Comput., Piscataway, NJ, USA, 1999, pp. 1931-1938.
13. J. Kennedy and R. Mendes, "Population structure and particle swarmperformance," in Proc. IEEE Congr. Evol. Comput., Honolulu, HI,USA, 2002, pp. 1671-1676.
14. P. N. Suganthan, "Particle swarm optimizer with neighborhood operator,"in Proc. IEEE Congr. Evol. Comput., Washington, DC, USA,1999, pp. 1958-1962.
15. X. Hu and R. C. Eberhart, "Multiobjective optimization using dynamicneighborhood particle swarm optimization," in Proc. IEEE Congr.Evol. Comput., Honolulu, HI, USA, 2002, pp. 1677-1681.
16. M. S. Arumugam and M. V. C. Rao, "On the improved performancesof the particle swarm optimization algorithms with adaptive parameters,cross-over operator sand root mean square (RMS) variants forcomputing optimal control of a class of hybrid systems," Appl. SoftComput., vol. 8, pp. 324-336, 2008.
17. B. Niu, Y. L. Zhu, and X. X. He, "Construction of fuzzy models fordynamic systems using multi-population cooperative particle swarmoptimizer," in Proc.Int. Conf. Fuzzy Syst. Knowl. Discovery, Changsha,China, 2005, pp. 987-1000.
18. B. Niu, Y. L. Zhu, and X. X. He, "Multi-population cooperative particleswarm optimization," in Proc. Eur. Conf. Advances Artif. Life,Canterbury, UK, 2005, pp. 874-883.
19. F. V. D. Bergh and A. P. Engelbrecht, "A cooperative approach to particleswarm optimization," IEEE Trans. Evol. Comput., vol. 8, no. 3,pp. 225-239, Jun. 2004.
20. S. He, Q. H.Wu, J. Y.Wen, J. R. Saunders, and R. C. Paton, "A particleswarm optimizer with passive congregation," Biosystems, vol. 78, no.1-3, pp. 135-147, 2004.
21. X. F. Xie, W. Zhang, and Z. Yang, "Hybrid particle swarm optimizerwith mass extinction," in Proc. Int. Conf. Commun., Circuits, Syst.,Chengdu, China, 2002, pp. 1170-1173.
22. M. Lbjerg, T. K. Rasmussen, and T. Krink, "Hybrid particle swarmoptimizer with breeding and subpopulations," in Proc. Genetic Evol.Comput. Conf., San Francisco, CA, USA, 2001, pp. 469-476.
23. Y.Wang, B. Li, T.Weise, J.Wang, B.Yuan, and Q. Tian, "Self- adaptivelearning based particle swarm optimization," Inf. Sci., vol. 181, no. 20,pp. 4515-4538, 2011.
24. J. J. Liang, A. K. Qin, and S. Baskar, "Comprehensive learning particleswarm optimizer for global optimization of multimodal function,"IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281-295, Jun. 2006.
25. B. Liu, L. Wang, and Y. Jin, "An effective PSO-based memetic algorithmfor flow shop scheduling," IEEE Trans. System, Man, Cybern.,Part B: Cybern., vol. 37, no. 1, pp. 985-997, 2007.
26. J. C. Bean, "Genetic algorithms and random keys for sequencing andoptimization," ORSA J. Computing, vol. 6, no. 2, pp. 154-160, 1994.
27. R. V. Bossche, K. Vanmechelen, and J. Broeckhove, "Cost- optimalscheduling in hybrid IaaS clouds for deadline constrained workload,"in Proc. IEEE Int. Conf. Cloud Comput., Miami, FL, 2010, pp.228-235.
28. S. He, L. Guo, and Y. Guo, "Real time elastic cloud management forlimited resources," in Proc. IEEE Int. Conf. Cloud Comput., Washington,DC, USA, 2011, pp. 622-629.
29. S. Doctor, G. K. Venayagamoorthy, and V. G. Gudise, "Optimal PSOfor collective robotic search applications," in Proc. IEEE Congr. Evol.Comput., San Diego, CA, USA, 2004, pp. 1390-1395.
30. Y. Shi and R. C. Eberhard, "Parameter selection in particle swarm optimization,"in Proc. Annu. Conf. Evol. Program., 1998, pp. 591-601.
31. A. Nathani, S. Chaudhary, and G. Somani, "Policy based resource allocationin IaaS cloud," Future Generation Comput. Syst., vol. 28, no.1, pp. 94-103, 2012.
32. C. Zhao, S. Zhang, Q. Liu, J. Xie, and J. Hu, "Independent tasksscheduling based on genetic algorithm in cloud computing," in Proc.Int. Conf. Wireless Commun., Netw., Mobile Comput., Marrakech,Morocco, 2009, pp. 1-4.
33. L. Li, "An optimistic differentiated service job scheduling system forcloud computing service users and providers," in Proc. Int. Conf. Multimediaand Ubiquitous Eng., Qingdao, China, 2009, pp. 295-299.
34. R. Jeyarani, N. Nagavent, and R. V. Ram, "Design and implementationof adaptive power-aware virtual machine provisioner (APA-VMP)using swarm intelligence," Future Generation Comput. Syst., vol. 28,no. 5, pp. 811-821, 2012.
35. X. Shi and Y. Zhao, "Dynamic resource scheduling and workflow managementin cloud computing," in Proc. Int. Conf. Web Inf. Syst. Eng.,Sydney, Australia, 2011, pp. 440-448.
36. C. Li and L. Li, "A distributed multiple dimensional QoS constrainedresource scheduling optimization policy in computational grid," J.Comput. Syst. Sci., vol. 72, no. 4, pp. 706-726, 2006.
37. Z. Lee, Y. Wang, and W. Zhou, "A dynamic priority scheduling algorithmon service request scheduling in cloud computing," in Proc.Int. Conf. Electron. Mech. Eng. Inf. Technol., Harbin, China, 2011, pp.4665-4669.
38. B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, "Virtualinfrastructure management in private and hybrid cloud," IEEE InternetComput., vol. 13, no. 5, pp. 14-22, 2009.
39. S. Chaisiri, B. Lee, and D. Niyato, "Optimal virtualmachine placementacross multiple cloud providers," in Proc. IEEE Asia-Pacific ServiceComput. Conf., Singapore, 2009, pp. 103-110.