IEEE Transactions on Parallel and Distributed Systems
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Workload Evolution on the Cornell Theory Center IBM SP2
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Memory Usage in the LANL CM-5 Workload
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Improved Utilization and Responsiveness with Gang Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Economic Scheduling in Grid Computing
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
A Comparison of Workload Traces from Two Production Parallel Machines
FRONTIERS '96 Proceedings of the 6th Symposium on the Frontiers of Massively Parallel Computation
On Advantages of Grid Computing for Parallel Job Scheduling
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Applying economic scheduling methods to Grid environments
Grid resource management
User group-based workload analysis and modelling
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Artificial Life
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
On the impact of reservations from the grid on planning-based resource management
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Parallel computer workload modeling with markov chains
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Future Generation Computer Systems
New challenges of parallel job scheduling
JSSPP'07 Proceedings of the 13th international conference on Job scheduling strategies for parallel processing
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
International Journal of Approximate Reasoning
Fuzzy scheduling with swarm intelligence-based knowledge acquisition for grid computing
Engineering Applications of Artificial Intelligence
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In this paper, we present a methodology for automatically generating online scheduling strategies for a complex scheduling objective with the help of real life workload data. The scheduling problem includes independent parallel jobs and multiple identical machines. The objective is defined by the machine provider and considers different priorities of user groups. In order to allow a wide range of objective functions, we use a rule based scheduling strategy. There, a rule system classifies all possible scheduling states and assigns an appropriate scheduling strategy based on the actual state. The rule bases are developed with the help of a Genetic Fuzzy System that uses workload data obtained from real system installations. We evaluate our new scheduling strategies again on real workload data in comparison to a probability based scheduling strategy and the EASY standard scheduling algorithm. To this end, we select an exemplary objective function that prioritizes some user groups over others.