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Ensemble-based network edge processing

Petri, Ioan, Zamani, Ali Reza, Balouek-Thomert, Daniel, Rana, Omer, Rezgui, Yacine and Parashar, Manish 2018. Ensemble-based network edge processing. Presented at: IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), Zurich, Switzerland, 17-20 Dec 2018. 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC). IEEE, pp. 133-142. 10.1109/UCC.2018.00022
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Abstract

Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Computer Science & Informatics
Publisher: IEEE
ISBN: 9781538655047
Date of First Compliant Deposit: 12 January 2019
Date of Acceptance: 12 October 2018
Last Modified: 17 Jan 2019 11:33
URI: http://orca.cf.ac.uk/id/eprint/118327

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