Adaptive neural network workflow management for Utility Management Systems

This paper focuses on grid performance optimization in large scale workflow applications with an intelligent workflow scheduling mechanism. Utility Management Systems (UMS) are managing very large numbers of workflows with very high resource requirements. This paper proposes a UMS scheduling archite...

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Bibliographic Details
Main Authors: Vukmirović, Srđan, Erdeljan, Aleksandar, Imre, Lendak, Čapko, Darko, Nedic, Nemanja
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Computer architecture
Dynamic scheduling
Job shop scheduling
Optimal scheduling
Performance evaluation
Processor scheduling
Online Access:Request full text
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Summary:This paper focuses on grid performance optimization in large scale workflow applications with an intelligent workflow scheduling mechanism. Utility Management Systems (UMS) are managing very large numbers of workflows with very high resource requirements. This paper proposes a UMS scheduling architecture which dynamically executes a scheduling algorithm using near real-time feedback about the current status of grid nodes. Workflow scheduling was performed with an artificial neural network (ANN). The network was trained in a system with three workflows. The case study presented in this paper shows results achieved in a three workflow system, as well as results achieved in a five workflow system where an adaptive ANN was used. The results testify that significant improvement of overall execution time can be achieved by adapting weights in the neural network.
DOI:10.1109/NEUREL.2010.5644102