DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks

Fang, C; Hu, Z; Meng, X; Tu, S; Wang, Z; Zeng, D; Ni, W; Guo, S; Han, Z

DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks

Fang, C Hu, Z Meng, X Tu, S Wang, Z Zeng, D Ni, W

Guo, S Han, Z

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2023, 72, (12), pp. 16195-16207
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Fang, C
dc.contributor.author	Hu, Z
dc.contributor.author	Meng, X
dc.contributor.author	Tu, S
dc.contributor.author	Wang, Z
dc.contributor.author	Zeng, D
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Guo, S
dc.contributor.author	Han, Z
dc.date.accessioned	2024-04-16T04:42:03Z
dc.date.available	2024-04-16T04:42:03Z
dc.date.issued	2023-12-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2023, 72, (12), pp. 16195-16207
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/177970
dc.description.abstract	With the proliferation of mobile devices (e.g., vehicles and smartphones), rich media content services from massive users lead to high network resource consumption and energy usage. How to effectively allocate heterogeneous network resources and achieve green content delivery is a major challenge to be addressed in urgency, especially when vehicular users are involved and the spatiotemporal distribution of the content requests can change drastically. In this article, we propose a new deep reinforcement learning (DRL)-Aided task offloading and resource allocation scheme, named TORA-DRL, to minimize power consumption in cloud-edge cooperation environments, where in-network caching and request aggregation are incorporated to reduce replicated transmissions of network contents. Based on the history of content requests and available network resources in the system, TORA-DRL jointly optimizes the decisions about task offloading, as well as computing, caching and communication resource allocation, adapting to the changes in network states and user requirements. Simulation results demonstrate that the proposed TORA-DRL solution converges fast and has much higher energy efficiency than the existing popular cloud-edge cooperation schemes under different network environments.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2023.3297362
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks
dc.type	Journal Article
utslib.citation.volume	72
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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	closed_access	*
dc.date.updated	2024-04-16T04:42:01Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	72
utslib.citation.issue	12

Abstract:

With the proliferation of mobile devices (e.g., vehicles and smartphones), rich media content services from massive users lead to high network resource consumption and energy usage. How to effectively allocate heterogeneous network resources and achieve green content delivery is a major challenge to be addressed in urgency, especially when vehicular users are involved and the spatiotemporal distribution of the content requests can change drastically. In this article, we propose a new deep reinforcement learning (DRL)-Aided task offloading and resource allocation scheme, named TORA-DRL, to minimize power consumption in cloud-edge cooperation environments, where in-network caching and request aggregation are incorporated to reduce replicated transmissions of network contents. Based on the history of content requests and available network resources in the system, TORA-DRL jointly optimizes the decisions about task offloading, as well as computing, caching and communication resource allocation, adapting to the changes in network states and user requirements. Simulation results demonstrate that the proposed TORA-DRL solution converges fast and has much higher energy efficiency than the existing popular cloud-edge cooperation schemes under different network environments.

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http://hdl.handle.net/10453/177970