Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Enhanced Algorithms for Multi-site Scheduling
GRID '02 Proceedings of the Third International Workshop on Grid Computing
The ANL/IBM SP Scheduling System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Parallel Job Scheduling: Issues and Approaches
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Metrics for Parallel Job Scheduling and Their Convergence
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
Prospects of collaboration between compute providers by means of job interchange
JSSPP'07 Proceedings of the 13th international conference on Job scheduling strategies for parallel processing
Optimised scheduling of grid resources using hybrid evolutionary algorithms
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Costs and benefits of load sharing in the computational grid
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Optimal Power Management for Server Farm to Support Green Computing
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Competitive coevolutionary learning of fuzzy systems for job exchange in computational grids
Evolutionary Computation
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In this paper, we introduce a methodology for the approximation of optimal solutions for a resource allocation problem in the domain of Grid scheduling on High Performance Computing systems. In detail, we review a real-world scenario with decentralized, equitable, and autonomously acting suppliers of compute power who wish to collaborate in the provision of their resources. We exemplarily apply NSGA-II in order to explore the bounds of maximum achievable benefit. To this end, appropriate encoding schemes and variation operators are developed while the performance is evaluated. The simulations are based upon recordings from real-world Massively Parallel Processing systems that span a period of eleven months and comprise approximately 100,000 jobs. By means of the obtained Pareto front we are able to identify bounds for the maximum benefit of Grid computing in a popular scenario. For the first time, this enables Grid scheduling researchers to rank their developed real-world strategies.