Abstract
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.
Highlights
Thanks to advances in wide-area network technologies and the low cost of computing resources, computational grids, emerging as attractive computing platforms, enable the sharing, selection, and aggregation of geographically distributed resources for solving largescale problems in science, engineering, and commerce
In this paper we propose the Hierarchical Artificial Neural Network for workflow scheduling in Utility Management Systems
The workflow scheduling component, which is responsible for starting appropriate workflows, uses the Artificial Neural Network for choosing the optimal scheduling strategy
Summary
Thanks to advances in wide-area network technologies and the low cost of computing resources, computational grids, emerging as attractive computing platforms, enable the sharing, selection, and aggregation of geographically distributed resources for solving largescale problems in science, engineering, and commerce. Processing and storage of measured data becomes a problem, since more and more measured values are introduced in the controlled system This problem could be solved by using the computer Grid. UMS have some special requirements, mainly because they have to communicate with end devices (sensors and actuators) and have to store very large volumes of time-series data about variable values. This makes workflow schedule control for UMS a special problem. A novel hierarchical neural network model is proposed in this paper It will solve the problem of workflow scheduling in large scale Utility Management Systems (UMS). The aim of this work is to provide novel system architecture and combine it with enhanced algorithms in order to boost the efficiency of the solution
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Intelligence Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
