Development of scheduling strategies with Genetic Fuzzy systems

Applied Soft Computing Journal

Volumn 8, Issue 1, 2008, Pages 706-721

  Franke, Carsten ^a   Hoffmann, Frank ^a   Lepping, Joachim ^a   Schwiegelshohn, Uwe ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Genetic Fuzzy system; Online scheduling; Scheduling algorithm development; Symbiotic Evolution

Indexed keywords

ALGORITHMS; GENETIC PROGRAMMING; ITERATIVE METHODS; ONLINE SYSTEMS; REAL TIME SYSTEMS; SCHEDULING;

GENETIC FUZZY SYSTEM; PARALLEL JOBS; RULE SYSTEM; SYMBIOTIC EVOLUTION;

FUZZY SYSTEMS;

EID: 34548484345     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.05.009     Document Type: Article

References (48)

1. Aggarwal A.K., and Kent R.D. An adaptive generalized scheduler for grid applications. Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications (HPCS'05). IEEE Computer Society (2005) 188-194
2. Albers S. Online algorithms: a survey. Math. Prog. 97 (2003) 3-26
3. Bäck T., and Schwefel H.-P. An overview of evolutionary algorithms for parameter optimization. Evolut. Comput. 1 1 (1993) 1-23
4. N. Beume, M. Emmerich, C. Ernemann, L. Schonemann, U. Schwiegelshohn, Scheduling Algorithm Development based on Complex Owner Defined Objectives. Technical Report CI-190/05, University Dortmund, Germany, 2005.
5. Beyer H.-G., and Schwefel H.-P. Evolution strategies-a comprehensive introduction. Nat. Comput. 1 1 (2002) 3-52
6. Bonarini A. Evolutionary learning of fuzzy rules: competition and cooperation. In: Pedrycz W. (Ed). Fuzzy Modelling: Paradigms and Practice (1996), Kluwer Academic Press 265-284
7. Buyya R., Abramson D., and Giddy J. An evaluation of economy-based resource trading and scheduling on computational power grids for parameter sweep applications. In: Hariri S., Lee C.A., and Raghavendra C.S. (Eds). Proceedings of The 2nd Workshop on Active Middleware Services (AMS 2000), In Conjunction with Ninth IEEE International Symposium on High Performance Distributed Computing (HPDC 2000). Pittsburgh (2000), Kluwer Academic Press, Norwell
8. Buyya R., Abramson D., Giddy J., and Stockinger H. Economic models for resource management and scheduling in grid computing. J. Concurr. Comput. Pract. Exp. (CCPE) 14 (2002) 1507-1542 (special issue on Grid Computing Environments)
9. Buyya R., Abramson D., and Venugopal S. The grid economy. Proc. IEEE 93 3 (2005) 698-714 (special issue on Grid Computing)
10. Chapin S.J., Cirne W., Feitelson D.G., Jones J.P., Leutenegger S.T., Schwiegelshohn U., Smith W., and Talby D. Benchmarks and standards for the evaluation of parallel job schedulers. In: Feitelson D.G., and Rudolph L. (Eds). Proceedings of the 5th Job Scheduling Strategies for Parallel Processing, vol. 1659 of Lecture Notes in Computer Science (LNCS) (1999), Springer 67-90
11. Deb K. Multi-Objective Optimization using Evolutionary Algorithms (2001), John Wiley & Sons, Chichester, UK
12. Ernemann C., Song B., and Yahyapour R. Scaling of workload traces. In: Feitelson D.G., Rudolph L., and Schwiegelshohn U. (Eds). Proceedings of the 9th Job Scheduling Strategies for Parallel Processing, vol. 2862 of Lecture Notes in Computer Science (LNCS) (2003), Springer 166-183
13. Ernemann C., and Yahyapour R. Grid resource management-state of the art and future trends. Chapter Applying Economic Scheduling Methods to Grid Environments (2003), Kluwer Academic Publishers pp. 491-506
14. Feitelson D.G. Memory usage in the LANL CM-5 workload. In: Feitelson D.G., and Rudolph L. (Eds). Proceedings of the 3rd Job Scheduling Strategies for Parallel Processing, vol. 1291 of Lecture Notes in Computer Science (LNCS) (1997), Springer 78-94
15. Feitelson D.G. Metric and workload effects on computer systems evaluation. Computer 36 9 (2003) 18-25
16. D.G. Feitelson, Parallel Workload Archive. http://www.cs.huji.ac.il/labs/parallel/workload/, April 2007.
17. Feitelson D.G., and Nitzberg B. Job characteristics of a production parallel scientific workload on the NASA ames iPSC/860. In: Feitelson D.G., and Rudolph L. (Eds). Proceedings of the 1st Job Scheduling Strategies for Parallel Processing, vol. 949 of Lecture Notes in Computer Science (LNCS) (1995), Springer 337-360
18. Feitelson D.G., and Weil A.M. Utilization and predictability in scheduling the IBM SP2 with backfilling. Proceedings of the 12th International Parallel Processing Symposium and the 9th Symposium on Parallel and Distributed Processing, vol. 12 (1998), IEEE Computer Society Press 542-547
19. Fogel L.J. Autonomous automata. Ind. Res. 4 (1962) 14-19
20. Garey M., and Graham R.L. Bounds for multiprocessor scheduling with resource constraints. SIAM J. Comput. 4 2 (1975) 187-200
21. Garey M., and Johnson D. Computers and Intractability: A Guide to the Theory of NP-Completeness (1979), Freeman, W.H. and Company
22. Gibbons R. A historical application profiler for use by parallel schedulers. In: Feitelson D.G., and Rudolph L. (Eds). Proceedings of the 3rd Job Scheduling Strategies for Parallel Processing, vol. 1291 of Lecture Notes in Computer Science (LNCS) (1997), Springer 58-77
23. Graham R.L. Bounds for certain multiprocessor anomalies. Bell Syst. Tech. J. 45 (1966) 1563-1581
24. Graham R.L., Lawler E.L., Lenstra J.K., and Rinnooy Kan A.H.G. Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discrete Math. 15 (1979) 287-326
25. Hall L., Shmoys D., and Wein J. Scheduling to minimize average completion time: off-line and on-line algorithms. Proceedings of the 7th SIAM Symposium on Discrete Algorithms. SIAM, Philadelphia (1996) 142-151
26. Hoffmann F. Evolutionary algorithms for fuzzy control system design. Proc. IEEE 89 9 (2001) 1318-1333
27. Holland J.H. Outline for a logical theory of adaptive systems. J. ACM 9 (1962) 297-314
28. Hotovy S. Workload evolution on the Cornell Theory Center IBM SP2. In: Feitelson D.G., and Rudolph L. (Eds). Proceedings of the 2nd Job Scheduling Strategies for Parallel Processing, vol. 1162 of Lecture Notes in Computer Science (LNCS) (1996), Springer 27-40
29. 3 fuzzy rule systems from data using evolution strategies. IEEE Trans. Syst. Man Cybern. 29 6 (1999) 829-845
30. Juang C.-F., Lin J.-Y., and Lin C.-T. Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans. Syst. Man Cybern. 30 2 (2000) 290-302
31. Kawaguchi T., and Kyan S. Worst case bound of an LRF schedule for the mean weighted flow-time problem. SIAM J. Comput. 15 4 (1986) 1119-1129
32. Lee C.B., Schwartzman Y., Hardy J., and Snavely A. Are user runtime estimates inherently inaccurate?. Proceedings of the 10th Job Scheduling Strategies for Parallel Processing, vol. 3277 of Lecture Notes in Computer Science (LNCS) (2005), Springer 253-263
33. Lifka D. The ANL/IBM SP scheduling system. In: Feitelson D.G., and Rudolph L. (Eds). Proceedings of the 1st Job Scheduling Strategies for Parallel Processing, vol. 949 of Lecture Notes in Computer Science (LNCS) (1995), Springer 295-303
34. Mu'alem A.W., and Feitelson D.G. Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Trans. Parallel Distrib. Syst. 12 6 (2001) 529-543
35. Paredis J. Coevolutionary computation. Artif. Life 2 4 (1995) 355-375
36. I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Ph.D. Thesis, TU Berlin, 1971.
37. Rechenberg I. Evolutionsstrategie-Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (1973), Frommann-Holzboog, Stuttgart
38. Schoknecht R., Spott M., Liekweg F., and Riedmiller M. Search space reduction for strategy learning in sequential decision processes. Proceedings of the 6th International Conference on Neural Information Processing, vol. 1 (1999) 148-153
39. H.-P. Schwefel. Evolutionsstrategie und numerische Optimierung, Ph.D. thesis, TU Berlin, 1975.
40. Schwefel H.-P. Evolution and Optimum Seeking (1995), John Wiley & Sons, New York
41. Schwiegelshohn U., and Yahyapour R. Improving first-come-first-serve job scheduling by gang scheduling. In: Feitelson D.G., Rudolph L., and Schwiegelshohn U. (Eds). Proceedings of the 4th Job Scheduling Strategies for Parallel Processing, vol. 1459 of Lecture Notes in Computer Science (LNCS) (1998), Springer 180-198
42. Schwiegelshohn U., and Yahyapour R. Fairness in parallel job scheduling. J. Schedul. 3 1 (2000) 297-320
43. S.F. Smith, A learning system based on genetic adaptive algorithms, Ph.D. Thesis, Department of Computer Science, University of Pittsburgh, 1980.
44. Song B., Ernemann C., and Yahyapour R. User Group-based Workload Analysis and Modeling. Proceedings of the International Symposium on Cluster Computing and the Grid (CCGRID2005) (2005), IEEE Computer Society Press (CD-ROM)
45. Späth H. The Cluster Dissection and Analysis: Theory, FORTRAN Programs Examples (1985), Prentice-Hall, Inc., Upper Saddle River, NJ, USA
46. Vercauteren T., Aggarwal P., Wang X., and Li T.-H. Hierarchical forecasting of web server workload using sequential Monte Carlo training. IEEE Trans. Signal Process. 55 4 (2007) 1286-1297
47. Webster S. A priority rule for minimizing weighted flow time in a class of parallel machine scheduling problems. Eur. J. Operat. Res. 70 (1993) 327-334
48. Windisch K., Lo V., Moore R., Feitelson D.G., and Nitzberg B. A comparison of workload traces from two production parallel machines. Proceedings of the 6th Symposium on Frontiers Massively Parallel Computing (1996), IEEE Computer Society Press 319-326