Development of scheduling strategies with Genetic Fuzzy systems

Franke, Carsten; Hoffmann, Frank; Lepping, Joachim; Schwiegelshohn, Uwe

doi:10.1016/j.asoc.2007.05.009

Cited by 39 publications

(16 citation statements)

References 19 publications

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“…We generate our evolutionary Fuzzy-Systems with a fixed number of N r = 10 rules, since previous studies of Franke et al (2008) revealed that rule bases consisting of five to ten rules yield good results. Because the whole rule base is encoded in one individual of length l, we optimize a problem with l ¼ N r Á ðN f Á 2 þ 1Þ ¼ 10 Á ð2 Á 2 þ 1Þ ¼ 50 parameters, see Eq.…”

Section: Evolutionary Fuzzy-system Configurationmentioning

confidence: 99%

“…Franke et al (2008) have shown for online scheduling problems that Fuzzy-Systems are able to learn good scheduling decisions for parallel machines, outperforming most static heuristics. Their combination with nature inspired optimization techniques promise high success potential as we have to deal with extremely large problem instances: Real world Grids consist of many participating sites and usually require the management and allocation of tens of thousands of jobs within comparatively short time periods.…”

Section: Introductionmentioning

confidence: 98%

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

et al. 2010

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We utilize a competitive coevolutionary algorithm (CA) in order to optimize the parameter set of a Fuzzy-System for job negotiation between CommunityGrids. 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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Section: Evolutionary Fuzzy-system Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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

et al. 2010

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“…We generate our genetic fuzzy systems with a fixed number of N r = 10 rules, since previous studies by Franke et al (2008) revealed that rule bases consisting of 5 to 10 rules yield good results. Because the whole rule base is encoded in one individual of length l, we optimize a problem with l = N r · (N f · 2 + 1) = 10 · (2 · 2 + 1) = 50 parameters.…”

Section: Cca and Genetic Fuzzy System Configurationmentioning

confidence: 99%

“…These states are modeled by fuzzy sets that are represented by simple membership functions. Such fuzzy system based scheduling techniques have been successfully applied to online scheduling problems before (see, e.g., Franke et al, 2008). They outperform most static scheduling heuristics due to their ability to flexibly adapt decisions to changing environments.…”

Section: Introductionmentioning

confidence: 98%

Competitive Coevolutionary Learning of Fuzzy Systems for Job Exchange in Computational Grids

Fölling

Grimme

Lepping

et al. 2009

Evolutionary Computation

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In our work, we address the problem of workload distribution within a computational grid. In this scenario, users submit jobs to local high performance computing (HPC) systems which are, in turn, interconnected such that the exchange of jobs to other sites becomes possible. Providers are able to avoid local execution of jobs by offering them to other HPC sites. In our implementation, this distribution decision is made by a fuzzy system controller whose parameters can be adjusted to establish different exchange behaviors. In such a system, it is essential that HPC sites can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. However, each site egoistically strives only for the minimization of its own jobs' response times regularly at the expense of other sites. This scenario is particularly suited for the application of a competitive coevolutionary algorithm: the fuzzy systems of the participating HPC sites are modeled as species that evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and grid setups, we show that opportunistic cooperation leads to significant improvements for each HPC site as well as for the overall system.

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“…Franke et al [22] presented a genetic -fuzzy system for automatically generating online scheduling strategies for a complex objective defined by a machine provider. The scheduling algorithm is based on a rule system, which classifies all possible scheduling states and assigns a corresponding scheduling strategy.…”

Section: Recent Applications Of Evolutionary Fuzzy Systems In Practicementioning

confidence: 99%

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

Abraham

Groşan

2008

Engineering Evolutionary Intelligent Systems

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Summary. Designing intelligent paradigms using evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has evolved during the last couple of decades. We also provide a review of some of the recent interesting evolutionary intelligent system frameworks reported in the literature.

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Development of scheduling strategies with Genetic Fuzzy systems

Cited by 39 publications

References 19 publications

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

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

Competitive Coevolutionary Learning of Fuzzy Systems for Job Exchange in Computational Grids

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

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