Multi-criteria optimal task allocation at the edge

Kolomvatsos, K. and Anagnostopoulos, C. (2019) Multi-criteria optimal task allocation at the edge. Future Generation Computer Systems, 93, pp. 358-372. (doi:10.1016/j.future.2018.10.051)

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Abstract

In Internet of Things (IoT), numerous nodes produce huge volumes of data that are subject of various processing tasks. Tasks execution on top of the collected data can be realized either at the edge of the network or at the Fog/Cloud. Their management at the network edge may limit the required time for concluding responses and return the final outcome/analytics to end-users or applications. IoT nodes, due to their limited computational and resource capabilities, can execute a limited number of tasks over the collected contextual data. A challenging decision is related to which tasks IoT nodes should execute locally. Each node should carefully select such tasks to maximize the performance based on the current contextual information, e.g., tasks’ characteristics, nodes’ load and energy capacity. In this paper, we propose an intelligent decision making scheme for selecting the tasks that will be locally executed. The remaining tasks will be transferred to peer nodes in the network or the Fog/Cloud. Our focus is to limit the time required for initiating the execution of each task by introducing a two-step decision process. The first step is to decide whether a task can be executed locally; if not, the second step involves the sophisticated selection of the most appropriate peer to allocate it. When, in the entire network, no node is capable of executing the task, it is, then, sent to the Fog/Cloud facing the maximum latency. We comprehensively evaluate the proposed scheme demonstrating its applicability and optimality at the network edge.

Item Type:Articles
Status:Published
Refereed:Yes
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
Journal Name:Future Generation Computer Systems
Publisher:Elsevier
ISSN:0167-739X
ISSN (Online):0167-739X
Published Online:02 November 2018
Copyright Holders:Copyright © 2018 Elsevier
First Published:First published in Future Generation Computer Systems 73: 358-372
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