Distributed Game-Theoretical Task Offloading for Mobile Edge Computing

Wang, En; Dong, Pengmin; Xu, Yuanbo; Li, Dawei; Wang, Liang; Yang, Yintang

doi:10.1109/mass52906.2021.00037

Cited by 4 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regardless of UEs' initial strategy selection and change order, G will converge to an NE within a finite number of iterations that can increase the payoff [33].…”

Section: Proof Of Corollary 1 the Potential Function Is Set To Bementioning

confidence: 99%

Joint Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Network

Tong

Wang

et al. 2022

Symmetry

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With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites’ MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS’s MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs’ indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs’ task offloading decisions and allocates the satellite’s and the BS’s computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes.

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“…Regardless of UEs' initial strategy selection and change order, G will converge to an NE within a finite number of iterations that can increase the payoff [33].…”

Section: Proof Of Corollary 1 the Potential Function Is Set To Bementioning

confidence: 99%