In-Network Decision Making Intelligence for Task Allocation in Edge Computing

Kolomvatsos, K. and Anagnostopoulos, C. (2018) In-Network Decision Making Intelligence for Task Allocation in Edge Computing. In: 30th International Conference on Tools with Artificial Intelligence (ICTAI 2018), Volos, Greece, 5-7 Nov 2018, pp. 655-662. ISBN 9781538674499 (doi: 10.1109/ICTAI.2018.00104)

166372.pdf - Accepted Version



Humongous contextual data are produced by sensing and computing devices (nodes) in distributed computing environments supporting inferential/predictive analytics. Nodes locally process and execute analytics tasks over contextual data. Demanding inferential analytics are crucial for supporting local real-time applications, however, they deplete nodes' resources. We contribute with a distributed methodology that pushes the task allocation decision at the network edge by intelligently scheduling and distributing analytics tasks among nodes. Each node autonomously decides whether the tasks are conditionally executed locally, or in networked neighboring nodes, or delegated to the Cloud based on the current nodes' context and statistical data relevance. We comprehensively evaluate our methodology demonstrating its applicability in edge computing environments.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos
Authors: Kolomvatsos, K., and Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2018 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3016540Intelligent Applications over Large Scale Data StreamsChristos AnagnostopoulosEuropean Commission (EC)N/AComputing Science