Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Volumn , Issue , 2015, Pages 870-876

  Li, Hai Hao ^a,b,c   Fu, Yu Wen ^a,b,c   Zhan, Zhi Hui ^a,b,c   Li, Jing Jing ^d  

^a SUN YAT SEN UNIVERSITY   (China)

^b Key Laboratory of Software Technology   (China)

^c STATE KEY LABORATORY OF MATHEMATICAL ENGINEERING AND ADVANCED COMPUTING   (China)

^d SOUTH CHINA NORMAL UNIVERSITY   (China)

Author keywords

cloud computing; particle swarm optimization; renumber; resource; scheduling

Indexed keywords

BIG DATA; CLOUD COMPUTING; COMPUTATIONAL COMPLEXITY; COSTS; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; SCHEDULING;

COMPUTATIONAL ABILITY; COST MINIMIZATION; DEADLINE CONSTRAINT; ENHANCED PARTICLE SWARM OPTIMIZATION; RENUMBER; RESOURCE; RESOURCE CHARACTERISTIC; WORKFLOW SCHEDULING;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84963527107     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2015.7256982     Document Type: Conference Paper

References (23)

1. P. Mell and T. Grance, "The NIST definition of cloud computing-recommendations of the National Institute of Standards and Technology," Special Publication 800-145, NIST, Gaithersburg, 2011.
2. G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, "Characterizing and profiling scientific workflows," Future Generation Comput. Syst., voL 29, no. 3, pp. 682-692,2012.
3. J. Yu and R Buyya, "A budget constrained scheduling of workt10w applications on utility grids using genetic algorithms," in Proc.[" Workshop Worliflows Support Large-Scale Sci., 2006, pp.l-l O.
4. M. Rahman, S. Venugopal, and R Buyya, "A dynamic critical pathalgorithm for scheduling scientific workt10w applications onglobal grids," in Proc. 3rd IEEE Int. Corif. e-Sci. Grid Comput., 2007, pp. 35-42.
5. W. N. Chen and.I. Zhang, "An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements," IEEE Transactions on Systems. Man., Cybernetics, Part C: Appl.Revs, vol. 39, no.l, pp. 29-43, Jan. 2009.
6. M. Mao and M. Humphrey, "Auto-scaling to minimize cost and meet application deadlines in cloud workflows," in Proc. Int. Corif. High Perform. Comput. Netw. Storage Anal., 2011, pp. 1-12.
7. M. Malawski, G. Juve, E. Deelman, and.I. Nabrzyski, "Cost-and deadline-constrained provisioning tor scientific workt10w ensembles in IaaS clouds," in Proc. Int. Can! High Perform. Comput., Netw., Storage Anal., 2012, pp. 1-11.
8. M. A. Rodriguez and R Buyya, "Deadline based resource provisioning and scheduling algorithm tor scientific workt1ows on clouds," IEEE Transactions on Cloud Computing, voL 2, no. 2, April-June 2014.
9. J. Kennedy and R Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Corif. Neural Networks, 1995, pp. 1942-1948.
10. M. Shen, Z. H. Zhan, W. N. Chen, Y.I. Gong,.I. Zhang, and Y Li, "Bi-velocity discrete particle swann optimization and its application to multicast routing problem in communication networks," IEEE Transactions on Industrial ElectroniCS, vol. 61, no. 12, pp. 7141-7151, Dec. 2014.
11. Y. H Li, Z H. Zhan, S. Lin, J. Zhang, and X N. Luo, "Competitive and cooperative particle swann optimization with intonnation sharing mechanism for global optimization problems," Information Sciences, voL 293, no. 1, pp. 370-382,2015.
12. Z H Zhan, J. Zhang, Y. Li, and H. Chung, "Adaptive particle swarm optimization," IEEE Transactions on Systems. Man., Cybernetics-Part B, voL 39, no. 6, pp. 1362-1381, Dec. 2009.
13. Z. H. Zhan,.I. Zhang, Y. Li, and Y. H. Shi, "Orthogonal learning particle swarm optimization," IEEE Transactions on Evolutionary Computation, voL 15, no. 6, pp. 832-847, Dec. 2011.
14. S. Pandey, L. L.Wu, S. M. Guru, and R. Buyya, "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments," in Proc. 24th IEEE Int. Corif. Advanced Info. Netw., Appl., 2010, pp. 400-407.
15. Z.I. Wu, Z. W. Ni, L. C. Gu, and X. Liu, "A revised discrete particle swarm optimization tor cloud workt1ow scheduling," in Proc. Int. Can! Comput. Intel/., Security, 2010, pp. 184-188.
16. W N. Chen and J. Zhang, "A set-based discrete PSO tor cloud workt1ow scheduling with user-defined QoS constraints," in Proc. IEEE Int. Can! Syst., Man, and Cybern., 2012, pp. 773-778.
17. L Z. Guo, S G. ZhaoP, S. G. Shen, and C. Y. Jiang, "A particle swarm optimization tor data placement strategy in cloud computing," Information Engineering and Applications, Lecture Notes in Electrical Engineering, pp. 323-330,2012.
18. S. J Xue and W. Wu, "Scheduling workt1ow in cloud computing based on hybrid particle swarm algorithm," TELKOMNIKA, vol.l0, no.7, pp. 1560-1566, Nov. 2012.
19. Z H. Zhan, J. Li, J. Cao, J. Zhang, H Chung, and Y. H. Shi, "Multiple populations for multiple objectives: A coevolutionary technique for solving multiobjective optimization problems," IEEE Transactions on Cybernetics, voL 43, no. 2, pp. 445-463, ApriL 2013.
20. Z. H. Zhan,.I. Zhang, Y. Li, O. Liu, S. K. Kwok, W. H. Ip, and O. Kaynak, "An efficient ant colony system based on receding horizon control tor the aircraft arrival sequencing and scheduling problem," IEEE Trans. Intel/. Transport. Syst., vol. 11, no. 2, pp. 399-412, Jun. 2010.
21. Y. L Li, Z H Zhan, Y. J. Gong, W. N. Chen, J. Zhang, and Y. Li, "Ditlerential evolution with an evolution path A DEEP evolutionary algorithm," IEEE Trans. on Cybernetics, DOl: 10.11091TCYB.2014.2360752, 2014.
22. M. D. Zhang, Z. H Zhan, J. J. Li, and J. Zhang, "Tournament selection based artificial bee colony algorithm with elitist strategy," in Proc. Corif. Technologies and Applications of Artijiciallntelligence, Taiwan, Nov. 2014, pp. 387-396.
23. Z. H. Zhan,.I. Zhang, Y. H. Shi, and H. L. Liu, "A modified brain storm optimization," in Proc. IEEE World Congr. Comput. Intell., Brisbane, Australia, Jun. 2012, pp. 1-8.