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Soft Computing

, Volume 15, Issue 7, pp 1255–1271 | Cite as

Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations

  • R. P. PradoEmail author
  • S. García-Galán
  • A. J. Yuste
  • J. E. Muñoz Expósito
Original Paper

Abstract

One of the most challenging problems when facing the implementation of computational grids is the system resources effective management commonly referred as to grid scheduling. A rule-based scheduling system is presented here to schedule computationally intensive Bag-of-Tasks applications on grids for virtual organizations. There exist diverse techniques to develop rule-base scheduling systems. In this work, we suggest the joining of a gathering and sorting criteria for tasks and a fuzzy scheduling strategy. Moreover, in order to allow the system to learn and thus to improve its performance, two different off-line optimization procedures based on Michigan and Pittsburgh approaches are incorporated to apply Genetic Algorithms to the fuzzy scheduler rules. A complex objective function considering users differentiation is followed as a performance metric. It not only provides the conducted system evaluation process a comparison with other classical approaches in terms of accuracy and convergence behaviour characterization, but it also analyzes the variation of a wide set of evolution parameters in the learning process to achieve the best performance.

Keywords

Grid computing Scheduling Fuzzy rule-based systems Evolutionary algorithms Genetic fuzzy systems 

Notes

Acknowledgments

This work has been financially supported by the Andalusian Government (Research Project P06-SEJ-01694).

References

  1. Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318CrossRefGoogle Scholar
  2. Booker LB, Goldberg DE, Holland JH (1989) Classifier systems and genetic algorithms. Artif Intell 40(1–3):235–282CrossRefGoogle Scholar
  3. Botta A, Lazzerini B, Marcelloni F, Stefanescu DC (2009) Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index. Soft Comput 13(5):437–449CrossRefGoogle Scholar
  4. Casanova H, Zagorodnov D, Berman F, Legrand A (2000) Heuristics for scheduling parameter sweep applications in grid environments. In: HCW ’00, Proceedings of the 9th Heterogeneous Computing Workshop. IEEE Computer Society, Washington, DC, p 349Google Scholar
  5. Casillas J, Carse B (2009) Special issue on genetic fuzzy systems: recent developments and future directions. Soft Comput 13(5):417–418CrossRefGoogle Scholar
  6. Casillas J, Martínez P, Benítez AD (2009) Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems. Soft Comput 13(5):451–465CrossRefGoogle Scholar
  7. Chen C-H, Hong T-P, Tseng VS (2008) A cluster-based genetic-fuzzy mining approach for items with multiple minimum supports. In: PAKDD’08, Proceedings of the 12th Pacific–Asia conference on advances in knowledge discovery and data mining. Springer, Berlin, pp 864–869Google Scholar
  8. Christodoulopoulos K, Gkamas V, Varvarigos EA (2007) Delay components of job processing in a grid: statistical analysis and modeling. In: ICNS ’07, Proceedings of the third International conference on networking and services. IEEE Computer Society, Washington, DC, p 23Google Scholar
  9. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. World Scientific Pub Co Inc., HackensackGoogle Scholar
  10. Davis LD, Mitchell M (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New YorkGoogle Scholar
  11. Delgado MR, Nagai EY, de Arruda LVR (2009) A neuro-coevolutionary genetic fuzzy system to design soft sensors. Soft Comput 13(5):481–495CrossRefGoogle Scholar
  12. Etminani K, Naghibzadeh M (2007) A min–min max–min selective algorihtm for grid task scheduling. In: Internet, 2007. ICI 2007. 3rd IEEE/IFIP International Conference in Central Asia on, pp 1–7Google Scholar
  13. Fernández A, García S, Luengo J, Bernadá-Mansilla E, Herrera F (2010) Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study. IEEE Trans Evol Comput. doi: 10.1109/TEVC.2009.2039140
  14. Foster I, Iamnitchi A (2003) On death, taxes, and the convergence of peer-to-peer and grid computing. In: 2nd International workshop on peer-to-peer systems (IPTPS 03), pp 118–128Google Scholar
  15. Foster I, Kesselman C (1999) The Grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  16. Franke C, Hoffmann F, Lepping J, Schwiegelshohn U (2008) Development of scheduling strategies with genetic fuzzy systems. Appl Soft Comput 8(1):706–721CrossRefGoogle Scholar
  17. Franke C, Lepping J, Schwiegelshohn U (2007a) Genetic fuzzy systems applied to online job scheduling. In: Fuzzy Systems Conference, 2007, FUZZ-IEEE 2007. IEEE International, pp 1–6Google Scholar
  18. Franke C, Lepping J, Schwiegelshohn U (2007b) Greedy scheduling with complex objectives. In: Computational intelligence in scheduling, 2007, SCIS ’07. IEEE Symposium on, pp 113–120Google Scholar
  19. Gacto MJ, Alcalá R, Herrera F (2009) Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems. Soft Comput 13(5):419–436CrossRefGoogle Scholar
  20. Garcíia S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959–977CrossRefGoogle Scholar
  21. Garey MR, Johnson DS (1979) Computers and Intractability: a guide to the theory of NP-completeness. W. H. Freeman & Co., New YorkzbMATHGoogle Scholar
  22. He X, Sun X, von Laszewski G (2003) Qos guided min–min heuristic for grid task scheduling. J Comput Sci Technol 18(4):442–451CrossRefzbMATHGoogle Scholar
  23. Holland JH (1985) Properties of the bucket brigade. In: Proceedings of the 1st International conference on genetic algorithms. L. Erlbaum Associates Inc., Hillsdale, pp 1–7Google Scholar
  24. Holland JH (1986) Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. Mach Learn 2:593–623Google Scholar
  25. Huang J, Jin H, Xie X, Zhang Q (2005) An approach to grid scheduling optimization based on fuzzy association rule mining. In: E-SCIENCE ’05, Proceedings of the first International conference on e-science and grid computing. IEEE Computer Society, Washington, DC, pp 189–195Google Scholar
  26. Joung C-S, Lee D-W, Sim K-B (1999) The fuzzy classifier system using the implicit bucket brigade algorithm. In: Intelligent Robots and Systems, 1999. IROS 99. Proceedings of the 1999 IEEE/RSJ International Conference on, vol 1. pp 83–87Google Scholar
  27. Juang C-F, Lin J-Y, Lin C-T (2000) Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans Syst Man Cybern Part B 30(2):290–302MathSciNetCrossRefGoogle Scholar
  28. Lee C-C (1989) Fuzzy logic in control systems: fuzzy logic controller-i. Technical Report UCB/ERL M89/90, EECS Department, University of California, BerkeleyGoogle Scholar
  29. Lee YC, Zomaya AY (2007) Practical scheduling of bag-of-tasks applications on grids with dynamic resilience. IEEE Trans Comput 56(6):815–825MathSciNetCrossRefGoogle Scholar
  30. Legrand A, Marchal L, Casanova H (2003) Scheduling distributed applications: the simgrid simulation framework. In: CCGRID ’03, Proceedings of the 3rd International symposium on cluster computing and the grid. IEEE Computer Society, Washington, DC, p 138Google Scholar
  31. Li H (2009) Workload dynamics on clusters and grids. J Supercomput 47(1):1–20CrossRefzbMATHGoogle Scholar
  32. Li T, Bollinger T, Breuer N, Wehle H-D (2004) Grid-based data mining in real-life business scenario. In: WI ’04, Proceedings of the 2004 IEEE/WIC/ACM International conference on web intelligence. IEEE Computer Society, Washington, DC, pp 611–614Google Scholar
  33. Litoiu M, Tadei R (2001) Fuzzy scheduling with application to real-time systems. Fuzzy Sets Syst 121(3):523–535MathSciNetCrossRefzbMATHGoogle Scholar
  34. Liu X, Chien AA (2004) Realistic large-scale online network simulation. In: SC ’04, Proceedings of the 2004 ACM/IEEE conference on Supercomputing. IEEE Computer Society, Washington, DC, p 31Google Scholar
  35. Maheswaran M, Ali S, Siegel HJ, Hensgen D, Freund RF (1999) Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J Parallel Distrib Comput 59(2):107–131CrossRefGoogle Scholar
  36. Mamdani EH et al (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of IEEE 121(12):1585–1588Google Scholar
  37. Marques de Sá JP (2008) Applied Statistics Using SPSS, STATISTICA, MATLAB and R. Springer, BerlinGoogle Scholar
  38. Mu’alem AW, Feitelson DG (2001) Utilization, predictability, workloads, and user runtime estimates in scheduling the ibm sp2 with backfilling. IEEE Trans Parallel Distrib Syst 12(6):529–543CrossRefGoogle Scholar
  39. Mucientes M, Vidal JC, Bugarín A, Lama M (2009) Processing time estimations by variable structure TSK rules learned through genetic programming. Soft Comput 13(5):497–509CrossRefGoogle Scholar
  40. Nojima Y, Ishibuchi H, Kuwajima I (2009) Parallel distributed genetic fuzzy rule selection. Soft Comput 13(5):511–519CrossRefGoogle Scholar
  41. Phatanapherom S, Uthayopas P, Kachitvichyanukul V (2003) Dynamic scheduling ii: fast simulation model for grid scheduling using hypersim. In: WSC ’03, Proceedings of the 35th conference on Winter simulation. Winter Simulation Conference, pp 1494–1500Google Scholar
  42. Prado RP, Galán SG, Yuste AJ, Expósito JEM, Santiago AJS, Bruque S (2009) Evolutionary fuzzy scheduler for grid computing. In: Lecture notes in computer science, vol 5517. Springer, Heidelberg, pp 286–293Google Scholar
  43. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar
  44. Sánchez L, Otero J, Couso I (2009) Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms. Soft Comput 13(5):467–479CrossRefzbMATHGoogle Scholar
  45. Schwiegelshohn U, Yahyapour R (1998) Analysis of first-come-first-serve parallel job scheduling. In: SODA ’98: Proceedings of the ninth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, pp 629–638Google Scholar
  46. Silva DPD, Cirne W, Brasileiro FV, Grande C (2003) Trading cycles for information: using replication to schedule bag-of-tasks applications on computational grids. In: Applications on computational grids, Proceedings of Euro-Par 2003, pp 169–180Google Scholar
  47. Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD thesis, PittsburghGoogle Scholar
  48. Song B, Ernemann C, Yahyapour R (2005) User group-based workload analysis and modelling. In: IEEE International Symposium on cluster computing and the grid, 2005. CCGrid 2005, vol 2. pp 953–961Google Scholar
  49. Spooner DP, Cao J, Jarvis SA, He L, Nudd GR (2005) Performance-aware workflow management for grid computing. Comput J 48(3):347–357CrossRefGoogle Scholar
  50. Sulistio A, Cibej U, Venugopal S, Robic B, Buyya R (2008) A toolkit for modelling and simulating data grids: an extension to gridsim. Concurr Comput: Pract Exper 20(13):1591–1609CrossRefGoogle Scholar
  51. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132zbMATHGoogle Scholar
  52. Tseng L, Chin Y, Wang S (2009) The anatomy study of high performance task scheduling algorithm for grid computing system. Comput Stand Interfaces 31(4):713–722CrossRefGoogle Scholar
  53. Weinberg SL, Abramowitz SK (2008) Statistics using SPSS: an integrative approach. Cambridge University Press, New YorkzbMATHGoogle Scholar
  54. Weng C, Lu X (2005) Heuristic scheduling for bag-of-tasks applications in combination with qos in the computational grid. Future Gener Comput Syst 21(2):271–280CrossRefGoogle Scholar
  55. Wieczorek M, Hoheisel A, Prodan R (2009) Towards a general model of the multi-criteria workflow scheduling on the grid. Future Gener Comput Syst 25(3):237–256CrossRefGoogle Scholar
  56. Xhafa F, Abraham A (2008) Meta-heuristics for grid scheduling problems. In: Metaheuristics for scheduling in distributed computing environments. Studies in Computational Intelligence, vol 146. Springer, Berlin, pp 1–37Google Scholar
  57. Xu Y, Liu J, Martínez L, Ruan D (2010) Some views on information fusion and logic based approaches in decision making under uncertainty. J Univ Comput Sci 16(1):3–19Google Scholar
  58. Yu J, Buyya R (2005) A taxonomy of workflow management systems for grid computing. J Grid Comput 3:171–200CrossRefGoogle Scholar
  59. Zhou J, Yu K-M, Chou C-H, Yang L-A, Luo Z-J (2007) A dynamic resource broker and fuzzy logic based scheduling algorithm in grid environment. In: ICANNGA ’07, Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I. Springer, Berlin, pp 604–613Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • R. P. Prado
    • 1
    Email author
  • S. García-Galán
    • 1
  • A. J. Yuste
    • 1
  • J. E. Muñoz Expósito
    • 1
  1. 1.Telecommunication Engineering DepartmentJaen UniversityJaenSpain

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