AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks

Fang, C; Meng, X; Hu, Z; Xu, F; Zeng, D; Dong, M; Ni, W

AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks

Fang, C Meng, X Hu, Z Xu, F Zeng, D Dong, M Ni, W

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Open Journal of the Computer Society, 2022, 3, pp. 162-171
Issue Date:: 2022-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.49 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Fang, C
dc.contributor.author	Meng, X
dc.contributor.author	Hu, Z
dc.contributor.author	Xu, F
dc.contributor.author	Zeng, D
dc.contributor.author	Dong, M
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.date.accessioned	2023-03-20T00:44:39Z
dc.date.available	2023-03-20T00:44:39Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Open Journal of the Computer Society, 2022, 3, pp. 162-171
dc.identifier.issn	2644-1268
dc.identifier.issn	2644-1268
dc.identifier.uri	http://hdl.handle.net/10453/167670
dc.description.abstract	To tackle a challenging energy efficiency problem caused by the growing mobile Internet traffic, this paper proposes a deep reinforcement learning (DRL)-based green content task offloading scheme in cloud-edge-end cooperation networks. Specifically, we formulate the problem as a power minimization model, where requests arriving at a node for the same content can be aggregated in its queue and in-network caching is widely deployed in heterogeneous environments. A novel DRL algorithm is designed to minimize the power consumption by making collaborative caching and task offloading decisions in each slot on the basis of content request information in previous slots and current network state. Numerical results show that our proposed content task offloading model achieves better power efficiency than the existing popular counterparts in cloud-edge-end collaboration networks, and fast converges to the stable state.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Open Journal of the Computer Society
dc.relation.isbasedon	10.1109/OJCS.2022.3206446
dc.rights	info:eu-repo/semantics/openAccess
dc.title	AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-20T00:44:37Z
pubs.publication-status	Published
pubs.volume	3

Abstract:

To tackle a challenging energy efficiency problem caused by the growing mobile Internet traffic, this paper proposes a deep reinforcement learning (DRL)-based green content task offloading scheme in cloud-edge-end cooperation networks. Specifically, we formulate the problem as a power minimization model, where requests arriving at a node for the same content can be aggregated in its queue and in-network caching is widely deployed in heterogeneous environments. A novel DRL algorithm is designed to minimize the power consumption by making collaborative caching and task offloading decisions in each slot on the basis of content request information in previous slots and current network state. Numerical results show that our proposed content task offloading model achieves better power efficiency than the existing popular counterparts in cloud-edge-end collaboration networks, and fast converges to the stable state.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167670