A multi-dimensional scheduling scheme in a Grid computing environment
Journal of Parallel and Distributed Computing
Modeling correlated workloads by combining model based clustering and a localized sampling algorithm
Proceedings of the 21st annual international conference on Supercomputing
Development of scheduling strategies with Genetic Fuzzy systems
Applied Soft Computing
Pro-active failure handling mechanisms for scheduling in grid computing environments
Journal of Parallel and Distributed Computing
On advantages of scheduling using genetic fuzzy systems
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
On grid performance evaluation using synthetic workloads
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing
Engineering Applications of Artificial Intelligence
Analysis and modeling of social influence in high performance computing workloads
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
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Knowledge about the workload is an important aspect for scheduling of resources as parallel computers or grid components. As the scheduling quality highly depends on the characteristics of the workload running on such resources, a representative workload model is significant for performance evaluation. Previous approaches on workload modelling mainly focused on methods that use statistical distributions to fit the overall workload characteristics. Therefore, the individual association and correlation to users or groups are usually lost. However, job scheduling for single parallel installations as well as for grid systems started to focus more on the quality of service for specific-user groups. Here, detailed knowledge of the individual user characteristic and preference is necessary for developing appropriate scheduling strategies. In the absence of a large information base of actual workloads, the adequate modelling of submission behaviors is sought. In this paper, we propose a new workload model, called MUGM (mixed user group model), which maintains the characteristics of individual user groups. The MUGM method has been further evaluated by simulations and shown to yield good results.