Alahmadi, AA, El-Gorashi, TEH and Elmirghani, JMH (2020) Energy Efficient and Delay Aware Vehicular Edge Cloud. In: 2020 22nd International Conference on Transparent Optical Networks (ICTON). 2020 22nd International Conference on Transparent Optical Networks (ICTON), 19-23 Jul 2020, Bari, Italy. IEEE ISBN 978-1-7281-8424-1
Abstract
Vehicular Edge Clouds (VECs) is a new distributed processing paradigm that exploits the revolution in the processing capabilities of vehicles to offer energy efficient services and improved QoS. In this paper we tackle the problem of processing allocation in a cloud- fog- VEC architecture by developing a joint optimization Mixed Integer Linear Programming (MILP) model to minimize power consumption, propagation delay, and queuing delay. The results show that while VEC processing can reduce the power consumption and propagation delay, VEC processing can increase the queuing delay because of the low data rate connectivity between the vehicles and the data source nodes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Delays, Power demand, Task analysis, Propagation delay, Cloud computing, Resource management, Energy efficiency |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/K503836/1 EPSRC (Engineering and Physical Sciences Research Council) EP/H040536/1 EPSRC (Engineering and Physical Sciences Research Council) EP/K016873/1 EPSRC (Engineering and Physical Sciences Research Council) EP/R511717/1 EPSRC (Engineering and Physical Sciences Research Council) EP/S016570/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Nov 2020 13:35 |
Last Modified: | 08 Dec 2020 00:44 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icton51198.2020.9203156 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168257 |