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Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems

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Abstract

We utilize a competitive coevolutionary algorithm (CA) in order to optimize the parameter set of a Fuzzy-System for job negotiation between Community-Grids. In a Community-Grid, users are submitting jobs to their local High Performance Computing (HPC) sites over time. Now, we assume that Community-Grids are interconnected such that the exchange of jobs becomes possible: Each Community strives for minimizing the response time for their own members by trying to distribute workload to other communities in the Grid environment. For negotiation purpose, a Fuzzy-System is used to steer each site’s decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. In such a system, it is essential that communities can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. That is, if one community egoistically refuses to execute foreign jobs regularly, other HPC sites suffer from overloading. This, on the long run, deteriorates the opportunity to utilize them for job delegation. Thus, the egoistic community will degrade its own average performance. This scenario is particularly suited for the application of a competitive CA: the Fuzzy-Systems of the participating communities are modeled as species, which evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each community and the overall system.

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Notes

  1. Multiple populations can also be used in ordinary evolution which are then called island models or species niching.

  2. In contrast to the common notation, we denote the mean of the GMF by γ to avoid conflicts with the parental population size of evolution strategies which we denote by μ.

References

  • Akbarzadeh MR, Mosavat I, Abbasi S (2003) Friendship modeling for cooperative co-evolutionary fuzzy systems: a hybrid GA-GP algorithm. In: 22nd International conference of the North American fuzzy information processing society, pp 61–66

  • Casillas J, Cordón O, Herrera F, Guervós JJM (2002) Cooperative coevolution for learning fuzzy rule-based systems. In: Selected papers from the 5th European conference on artificial evolution. Springer, London, pp. 311–322

  • Cordon O, del Jesus M-J, Herrera F (1999) A proposal on reasoning methods in fuzzy rule-based classification systems. Int J Approx Reason 20(1):21–45

    Google Scholar 

  • Cordon O, Gomide FAC, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141(1):5–31

    Article  MathSciNet  MATH  Google Scholar 

  • Delgado MR, Von Zuben F, Gomide F (2004) Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach. Fuzzy Sets Syst 141(1):89–106

    Article  MathSciNet  MATH  Google Scholar 

  • Donato JM, Barbieri E (1995) Mathematical representation of fuzzy membership functions. In: Proceedings of the 27th southeastern symposium on system theory. IEEE Computer Society, Washington, DC, USA, pp 290

  • Ernemann C, Hamscher V, Yahyapour R (2004) Benefits of global grid computing for job scheduling. In: Proceedings of the fifth IEEE/ACM international workshop on grid computing (GRID’04). IEEE Computer Society, pp 374–379

  • Foelling A, Grimme C, Lepping J, Papaspyrou A (2009) Co-evolving fuzzy rule sets for job exchange in computational grids. In: Proceedings of the 2009 IEEE international conference on fuzzy systems. IEEE Press, NJ, pp 1683–1688

  • Franke C, Hoffmann F, Lepping J, Schwiegelshohn U (2008) Development of scheduling strategies with genetic fuzzy systems. Appl Soft Comput 8(1):706–721

    Article  Google Scholar 

  • Grimme C, Langhammer T, Papaspyrou A, Schintke F (2007a) Negotiation-based choreography of data-intensive applications in the C3Grid Project. In: Proceedings of the German e-Science Conference (GES), Baden-Baden, Germany. Max-Planck Society (online)

  • Grimme C, Lepping J, Papaspyrou A (2007b) Exploring the behavior of building blocks for multi-objective variation operator design using predator–prey dynamics. In: Thierens D et al. (eds) Prococeedings of the genetic and evolutionary computation conference (GECCO 2007). ACM Press, London, pp 805–812

  • Herrera F (2005) Genetic fuzzy systems: status, critical considerations and future directions. Int J Comput Intell Res 1(1):5–31

    Google Scholar 

  • Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1(1):27–46

    Article  MathSciNet  Google Scholar 

  • Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Phys D 42(1–3):228–234

    Article  Google Scholar 

  • Ishibuchi H, Nakashima T, Morisawa T (1999) Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets Syst 103(2):223–238

    Article  Google Scholar 

  • 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 30(2):290–302

    Article  MathSciNet  Google Scholar 

  • Nejad SK, Najafi-Ardabili A, Akbarzadeh-T MR, Moeinzadeh M-H (2008) A co-evolutionary fuzzy system for real-time signal pre-processing. in: Asia international conference on modelling and simulation, pp 620–624

  • Olsson B (2001) Co-evolutionary search in asymmetric spaces. Inf Sci 133(3–4):103–125

    Article  MATH  Google Scholar 

  • Paredis J (1995) Coevolutionary computation. Artif Life 2(4):355–375

    Article  Google Scholar 

  • Paredis J (2000) Evolutionary computation 2: advanced algorithms and operators, chapter coevolutionary Algorithms. Institute of Physics Publishing, Bristol, pp 224–238

  • Peña-Reyes CA, Sipper M (2001) Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling. IEEE Trans Fuzzy Syst 9(5):727–737

    Article  Google Scholar 

  • Potter MA, De Jong KA (2000) Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evol Comp 8(1):1–29

    Article  Google Scholar 

  • Rosin CD, Belew RK (1997) New methods for competitive coevolution. Evol Comput 5(1):1–29

    Article  Google Scholar 

  • Schwefel H-P (1995) Evolution and optimum seeking. Wiley, New York

    Google Scholar 

  • Schwiegelshohn U, Yahyapour R (1998) Analysis of first-come-first-serve parallel job scheduling. In: Proceedings of the 9th SIAM symposium on discrete algorithms (SODA98). SIAM, Philadelphia, pp 629–638

  • Schwiegelshohn U, Yahyapour R (2000) Fairness in parallel job scheduling. J Sched 3(5):297–320

    Article  MathSciNet  MATH  Google Scholar 

  • Smarr L (2004) Grids in context. In: The grid: blueprint for a future computing infrastructure, 2nd edn. Morgan Kaufman, San Francisco, pp 3–12

  • Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD thesis, Department of Computer Science, University of Pittsburgh

  • Walter I, Gomide F (2009) Multiagent coevolutionary genetic fuzzy system to develop bidding strategies in electricity markets: computational economics to assess mechanism design. Evol Intell 2:53–71

    Article  Google Scholar 

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Authors and Affiliations

  1. Robotics Research Institute, TU Dortmund University, 44221, Dortmund, Germany

    Alexander Fölling, Christian Grimme, Joachim Lepping & Alexander Papaspyrou

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  1. Alexander Fölling
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  2. Christian Grimme
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  3. Joachim Lepping
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  4. Alexander Papaspyrou
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Correspondence to Joachim Lepping.

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Fölling, A., Grimme, C., Lepping, J. et al. Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems. Soft Comput 15, 2375–2387 (2011). https://doi.org/10.1007/s00500-010-0667-y

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