Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation

Daqing Zhang; Haifeng Sun

doi:10.1007/978-3-031-54521-4_22

Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation

Daqing Zhang
, Haifeng Sun

Southwest University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Mobile Edge Computing (MEC) is a promising technology that provides computing services at the edge of wireless networks to reduce the latency and the energy consumption for Smart Mobile Devices (SMDs). Additionally, the Ultra-Dense Network (UDN) will play a key role in providing high transmission capacity for SMDs in 5G networks. In order to improve the edge cloud efficiency within limited communication and computing resources, this paper proposes a joint task offloading and resource allocation scheme collaborated between cloud computing and edge computing in the UDN. Since wireless backhaul is more economical than expensive wired backhaul deployments, we consider the mixed deployment of either wired or wireless backhaul between each Small Base Station (SBS) and the Macro Base Station (MBS) in UDN scenarios, then formulate an optimization problem to minimize the system-wide computation overhead, and apply the Linear Decreasing Weight Particle Swarm Optimization (LDWPSO) algorithm to solve the problem. Numerical experiments validate the effectiveness of our proposed scheme compared to other baseline schemes.

Original language	English
Title of host publication	Collaborative Computing
Subtitle of host publication	Networking, Applications and Worksharing - 19th EAI International Conference, CollaborateCom 2023, Proceedings
Editors	Honghao Gao, Xinheng Wang, Nikolaos Voros
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	403-420
Number of pages	18
ISBN (Print)	9783031545207
DOIs	https://doi.org/10.1007/978-3-031-54521-4_22
Publication status	Published - 1 Jan 2024
Externally published	Yes
Event	19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023 - Corfu, Greece Duration: 4 Oct 2023 → 6 Oct 2023

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	561 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023
Country/Territory	Greece
City	Corfu
Period	4/10/23 → 6/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Mobile Edge Computing
Ultra-Dense Network
resource allocation
task offloading
wireless backhaul

Access to Document

10.1007/978-3-031-54521-4_22

Cite this

Zhang, D., & Sun, H. (2024). Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation. In H. Gao, X. Wang, & N. Voros (Eds.), Collaborative Computing: Networking, Applications and Worksharing - 19th EAI International Conference, CollaborateCom 2023, Proceedings (pp. 403-420). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 561 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-54521-4_22

Zhang, Daqing ; Sun, Haifeng. / Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links : Joint Task Offloading and Resource Allocation. Collaborative Computing: Networking, Applications and Worksharing - 19th EAI International Conference, CollaborateCom 2023, Proceedings. editor / Honghao Gao ; Xinheng Wang ; Nikolaos Voros. Springer Science and Business Media Deutschland GmbH, 2024. pp. 403-420 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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abstract = "Mobile Edge Computing (MEC) is a promising technology that provides computing services at the edge of wireless networks to reduce the latency and the energy consumption for Smart Mobile Devices (SMDs). Additionally, the Ultra-Dense Network (UDN) will play a key role in providing high transmission capacity for SMDs in 5G networks. In order to improve the edge cloud efficiency within limited communication and computing resources, this paper proposes a joint task offloading and resource allocation scheme collaborated between cloud computing and edge computing in the UDN. Since wireless backhaul is more economical than expensive wired backhaul deployments, we consider the mixed deployment of either wired or wireless backhaul between each Small Base Station (SBS) and the Macro Base Station (MBS) in UDN scenarios, then formulate an optimization problem to minimize the system-wide computation overhead, and apply the Linear Decreasing Weight Particle Swarm Optimization (LDWPSO) algorithm to solve the problem. Numerical experiments validate the effectiveness of our proposed scheme compared to other baseline schemes.",

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Zhang, D & Sun, H 2024, Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation. in H Gao, X Wang & N Voros (eds), Collaborative Computing: Networking, Applications and Worksharing - 19th EAI International Conference, CollaborateCom 2023, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 561 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 403-420, 19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023, Corfu, Greece, 4/10/23. https://doi.org/10.1007/978-3-031-54521-4_22

Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation. / Zhang, Daqing; Sun, Haifeng.
Collaborative Computing: Networking, Applications and Worksharing - 19th EAI International Conference, CollaborateCom 2023, Proceedings. ed. / Honghao Gao; Xinheng Wang; Nikolaos Voros. Springer Science and Business Media Deutschland GmbH, 2024. p. 403-420 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 561 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

PY - 2024/1/1

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N2 - Mobile Edge Computing (MEC) is a promising technology that provides computing services at the edge of wireless networks to reduce the latency and the energy consumption for Smart Mobile Devices (SMDs). Additionally, the Ultra-Dense Network (UDN) will play a key role in providing high transmission capacity for SMDs in 5G networks. In order to improve the edge cloud efficiency within limited communication and computing resources, this paper proposes a joint task offloading and resource allocation scheme collaborated between cloud computing and edge computing in the UDN. Since wireless backhaul is more economical than expensive wired backhaul deployments, we consider the mixed deployment of either wired or wireless backhaul between each Small Base Station (SBS) and the Macro Base Station (MBS) in UDN scenarios, then formulate an optimization problem to minimize the system-wide computation overhead, and apply the Linear Decreasing Weight Particle Swarm Optimization (LDWPSO) algorithm to solve the problem. Numerical experiments validate the effectiveness of our proposed scheme compared to other baseline schemes.

AB - Mobile Edge Computing (MEC) is a promising technology that provides computing services at the edge of wireless networks to reduce the latency and the energy consumption for Smart Mobile Devices (SMDs). Additionally, the Ultra-Dense Network (UDN) will play a key role in providing high transmission capacity for SMDs in 5G networks. In order to improve the edge cloud efficiency within limited communication and computing resources, this paper proposes a joint task offloading and resource allocation scheme collaborated between cloud computing and edge computing in the UDN. Since wireless backhaul is more economical than expensive wired backhaul deployments, we consider the mixed deployment of either wired or wireless backhaul between each Small Base Station (SBS) and the Macro Base Station (MBS) in UDN scenarios, then formulate an optimization problem to minimize the system-wide computation overhead, and apply the Linear Decreasing Weight Particle Swarm Optimization (LDWPSO) algorithm to solve the problem. Numerical experiments validate the effectiveness of our proposed scheme compared to other baseline schemes.

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PB - Springer Science and Business Media Deutschland GmbH

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Zhang D, Sun H. Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation. In Gao H, Wang X, Voros N, editors, Collaborative Computing: Networking, Applications and Worksharing - 19th EAI International Conference, CollaborateCom 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 403-420. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-54521-4_22