文件名称:Optimization-of-workflow-scheduling-in-Utility-Ma
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Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it
can be used for executing large scale applications. Utility Management Systems execute very large numbers of
workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism
that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two
Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the
Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad,
Serbia. Performance tests show that significant improvement of overall execution time can be achieved by
Hierarchical Artificial Neural Networks.
can be used for executing large scale applications. Utility Management Systems execute very large numbers of
workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism
that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two
Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the
Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad,
Serbia. Performance tests show that significant improvement of overall execution time can be achieved by
Hierarchical Artificial Neural Networks.
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Optimization of workflow scheduling in Utility Management System with hierarchical neural.pdf