Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wire...

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Bibliographic Details
Published in:IEEE/ACM transactions on networking 2016-10, Vol.24 (5), p.2795-2808
Main Authors: Chen, Xu, Jiao, Lei, Li, Wenzhong, Fu, Xiaoming
Format: Article
Language:English
Subjects:
Cloud computing
Computation offloading
Computational modeling
Decision making
Economic models
Game theory
Games
Mobile communication
Mobile handsets
mobile-edge cloud computing
Nash equilibrium
Wireless communication
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Summary:Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.
ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2015.2487344