Analysing quality of resilience in Fish4Knowledge video analysis workflows

Nadarajan, Gayathri, Yang, Cheng-Lin, Chen-Burger, Yun-Heh, Tolosana-Calasanz, Rafael and Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 2013. Analysing quality of resilience in Fish4Knowledge video analysis workflows. Presented at: 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC 2013), Dresden, Germany, 9-12 December 2013. Published in: Buyya, R., Schill, A. and Spillner, J. eds. Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC 2013). Los Alamitos, CA: IEEE, pp. 405-410. 10.1109/UCC.2013.81

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Abstract

The Fish4Knowledge (F4K) project involves analysing video generated from multiple camera feeds to support environmental and ecological assessment. A workflow engine is utilised in the project which deals with on-demand user queries and batch queries, selection of a suitable computing platform on which to enact the workflow along with a selection of suitable software modules to use to support analysis. A workflow monitor is also made use of, which handles the seamless execution and error monitoring of jobs on a heterogeneous computing platform. End users of such workflow generally include marine biologists, who are often primarily interested in the accuracy, performance and resilience of the workflows they execute. We describe how such users can be provided with possible workflow alternatives that trade off these three characteristics, based on previously recorded (historical) data. We describe a Quality of Resilience (QoR) metric that can be associated with multiple workflow alternatives, that enable such users to make more informed decisions about which alternative to choose.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	IEEE
ISBN:	9780769551524
Last Modified:	25 Oct 2022 09:40
URI:	https://orca.cardiff.ac.uk/id/eprint/59564

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