Alexander Fölling

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In this paper, we address the problem of finding workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a non-invasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and(More)
Scheduling in computational grids addresses the allocation of computing jobs to globally distributed compute resources. In a frequently changing resource environment, scheduling decisions have to be made rapidly. Depending on both the job properties and the current state of the resources those decisions 1 ha l-0 07 58 20 8, v er si on 1 29 N ov 2 01 2(More)
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(More)
In this paper, we address the problem of finding well-performing workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a noninvasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC)(More)
We utilize a competitive co-evolutionary 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(More)
To the most wonderful human being, my wife Rebecca, I gratefully dedicate this thesis. I would like to thank my advisor Professor Herbert Heuer for his continuous support, and for all the time, effort and thought he invested in my training. I enjoyed every brain storming session we had together and look forward to many more to come. Thanks to Professor(More)