[1] Zhang Jing, Xia Weiwei, Huang Bonan, Zou Qian, et al. Joint resource allocation scheme based on evolutionary gamefor mobile edge computing [J]. Journal of Southeast University (English Edition), 2018, 34 (4): 415-422. [doi:10.3969/j.issn.1003-7985.2018.04.001]

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Joint resource allocation scheme based on evolutionary gamefor mobile edge computing()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

34

Issue:

2018 4

Page:

415-422

Research Field:

Information and Communication Engineering

Publishing date:

2018-12-20

Info

Title:

Joint resource allocation scheme based on evolutionary gamefor mobile edge computing

Author(s):

Zhang Jing;  Xia Weiwei;  Huang Bonan;  Zou Qian;  Shen Lianfeng

National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Keywords:

mobile edge computing;  service provider selection;  joint resource allocation;  evolutionary game

PACS:

TN929.5

DOI:

10.3969/j.issn.1003-7985.2018.04.001

Abstract:

To satisfy mobile terminals’(MTs)offloading requirements and reduce MTs’ cost, a joint cloud and wireless resource allocation scheme based on the evolutionary game(JRA-EG)is proposed for overlapping heterogeneous networks in mobile edge computing environments. MTs that have tasks offloading requirements in the same service area form a population. MTs in one population acquire different wireless and computation resources by selecting different service providers(SPs). An evolutionary game is formulated to model the SP selection and resource allocation of the MTs. The cost function of the game consists of energy consumption, time delay and monetary cost. The solutions of evolutionary equilibrium(EE)include the centralized algorithm based on replicator dynamics and the distributed algorithm based on Q-learning. Simulation results show that both algorithms can converge to the EE rapidly. The differences between them are the convergence speed and trajectory stability. Compared with the existing schemes, the JRA-EG scheme can save more energy and have a smaller time delay when the data size becomes larger. The proposed scheme can schedule the wireless and computation resources reasonably so that the offloading cost is reduced efficiently.

References:

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Memo

Memo:

Biographies: Zhang Jing(1993—), female, Ph.D. candidate; Xia Weiwei(corresponding author), female, doctor, associate professor, wwxia@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.61741102, 61471164).
Citation: .Zhang Jing, Xia Weiwei, Huang Bonan, et al. Joint resource allocation scheme based on evolutionary game for mobile edge computing[J].Journal of Southeast University(English Edition), 2018, 34(4):415-422.DOI:10.3969/j.issn.1003-7985.2018.04.001.

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