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)

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: Anagnostopoulos, Dr Christos and Alghamdi, Ibrahim Ahmed I 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172888		Network Measurement as a Service (MaaS)	Dimitrios Pezaros	Engineering and Physical Sciences Research Council (EPSRC)	EP/N033957/1	Computing Science