Multi-objective workflow grid scheduling based on discrete particle swarm optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7076 LNCS, Issue PART 1, 2011, Pages 183-190

  Garg, Ritu ^a   Singh, Awadhesh Kumar ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION OVERHEADS; COMPUTING INFRASTRUCTURES; DISCRETE PARTICLE SWARM OPTIMIZATION; EFFICIENT SCHEDULING; EXECUTION TIME; GRID SCHEDULING; GRID-BASED; HIGH PERFORMANCE COMPUTING; MAKESPAN; MULTI OBJECTIVE; MULTIPLE SOLUTIONS; NSGA-II; OPTIMAL SOLUTIONS; OPTIMIZED SCHEDULE; SIMULATION ANALYSIS; TOTAL COSTS; WORKFLOW EXECUTION;

COMPUTER SOFTWARE SELECTION AND EVALUATION; MULTIOBJECTIVE OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); SCHEDULING ALGORITHMS;

GRID COMPUTING;

EID: 84555196726     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-27172-4_23     Document Type: Conference Paper

References (20)

1. Abraham, A., Liu, H., Zhang, W., Chang, T.G.: Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm, pp. 500-507. Springer, Heidelberg (2006)
2. Attiya, G., Hamam, Y.: Task allocation for maximizing reliability of distributed systems: A simulated annealing approach. Journal of Parallel and Distributed Computing 66, 1259-1266 (2006)
3. Braun, T.D., Siegal, H.J., Beck, N.: A comparision of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing 61, 810-837 (2001)
4. Buyya, R., Venugopal, S.: A Gentle Introduction to Grid Computing and Technologies. CSI Communications 29, 9-19 (2005)
5. Buyya, R., Murshed, M.: GridSim: A Toolkit for Modeling and Simulation of Grid Resource Management and Scheduling, vol. 14, pp. 1175-1220 (2002), http://www.buyya.com/gridsim
6. Chen, W.H., Lin, C.S.: A hybrid heuristic to solve a task allocation problem. Computers & Operations Research 27, 287-303 (2000)
7. Deb, K., Pratap, A., Aggarwal, S., Meyarivan, T.: A Fast Elitist Multi-Objective Genetic Algorithm. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849-858. Springer, Heidelberg (2000)
8. Deb, K., Jain, S.: Running Performance Metrics for Evolutionary Multi-objective Optimization. In: Proceedings of Simulated Evolution and Learning (SEAL 2002), pp. 13-20 (2002)
9. Wieczorek, M., Prodan, R., Fahringer, T.: Scheduling of Scientific Workflows in the ASKALON Grid Environment. SIGMOD 34(3), 56-62 (2005)
10. Haluk, T., Hariri, S., Wu, M.Y.: Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on Parallel and Distributed Systems 13, 260-274 (2002)
11. Izakian, H., Tork Ladani, B., Zamanifar, K., Abraham, A.: A Novel Particle Swarm Optimization Approach for Grid Job Scheduling. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds.) ICISTM 2009. CCIS, vol. 31, pp. 100-109. Springer, Heidelberg (2009)
12. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948 (1995)
13. Li, X.: A Non-dominated Sorting Particle Swarm Optimizer for Multi-objective Optimization. In: Proceeding of Genetic and Evolutionary Computation Conference 2003 (GECCO 20003), Chicago, USA (2003)
14. Ritchie, G., Levine, J.: A fast, effective local search for scheduling independent jobs in heterogeneous computing environments, Technical report, Centre for Intelligent Systems and their Applications, School of Informatics, University of Edinburgh (2003)
15. Subrata, R., Zomaya, Y.A., Landfeldt, B.: Artificial life techniques for load balancing in computational grids. Journal of Computer and System Sciences 73, 1176-1190 (2007)
16. Tsiakkouri, E., Sakellariou, R., Zhao, H., Dikaiakos, M.D.: Scheduling Workflows with Budget Constraints. In: CoreGRID Integration Workshop Pisa, Italy (2005)
17. Zhao, S.Z., Zhao, P.N.: Two-lbests Based Multi-objective Particle Swarm Optimizer. Engineering Optimization 43, 1-17 (2011)
18. Coello, C.A.C., Pulido, G., Lechuga, M.: Handling multi-objective with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256-279 (2004)
19. Praveen, K., Das, S., Welch, S.M.: Multi-Objective Hybrid PSO Using ε-Fuzzy Dominance. In: Proceeding of Genetic and Evolutionary Computation Conference (GECCO 2007), London, UK (2007)
20. Yu, J., Buyya, R.: Scheduling Scientific Workflow Applications with Deadline and Budget constraints using Genetic Algorithms. Scientific Programming Journal 14(1), 217-230 (2006)