Data quality-aware task offloading in mobile edge computing: an optimal stopping theory approach

Alghamdi, I., Anagnostopoulos, C. and Pezaros, D. P. (2021) Data quality-aware task offloading in mobile edge computing: an optimal stopping theory approach. Future Generation Computer Systems, 117, pp. 462-479. (doi: 10.1016/j.future.2020.12.017)

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Abstract

An important use case of the Mobile Edge Computing (MEC) paradigm is task and data offloading. Computational offloading is beneficial for a wide variety of mobile applications on different platforms including autonomous vehicles and smartphones. With the envision deployment of MEC servers along the roads and while mobile nodes are moving and having certain tasks (or data) to be offloaded to edge servers, choosing an appropriate time and an ideally suited MEC server to guarantee the Quality of Service (QoS) is challenging. We tackle the data quality-aware offloading sequential decision making problem by adopting the principles of Optimal Stopping Theory (OST) to minimize the expected processing time. A variety of OST stochastic models and their applications to the offloading decision making problem are investigated and assessed. A performance evaluation is provided using simulation approach and real world data sets together with the assessment of baseline deterministic and stochastic offloading models. The results show that the proposed OST models can significantly minimize the expected processing time for analytics task execution and can be implemented in the mobile nodes efficiently.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Alghamdi, Ibrahim Ahmed I and Anagnostopoulos, Dr Christos and Pezaros, Professor Dimitrios
Authors: Alghamdi, I., Anagnostopoulos, C., and Pezaros, D. P.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Future Generation Computer Systems
Publisher:Elsevier
ISSN:0167-739X
ISSN (Online):1872-7115
Published Online:24 December 2020
Copyright Holders:Copyright © 2020 Elsevier B.V.
First Published:First published in 117:462-479
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
172888Network Measurement as a Service (MaaS)Dimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/N033957/1Computing Science