Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Co-evolutionary search in asymmetric spaces
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Development of scheduling strategies with Genetic Fuzzy systems
Applied Soft Computing
New methods for competitive coevolution
Evolutionary Computation
Discovering performance bounds for grid scheduling by using evolutionary multiobjective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Exploring the explorative advantage of the cooperative coevolutionary (1+1) EA
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The gain of resource delegation in distributed computing environments
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
Infrastructure Federation Through Virtualized Delegation of Resources and Services
Journal of Grid Computing
Knowledge discovery for scheduling in computational grids
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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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.