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Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

1
School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athina, Greece
2
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(5), 118; https://doi.org/10.3390/fi11050118
Received: 12 April 2019 / Revised: 10 May 2019 / Accepted: 13 May 2019 / Published: 21 May 2019
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Abstract

Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users. View Full-Text
Keywords: software defined networks; mobile edge computing; reinforcement learning; stochastic learning automata; game theory; data offloading; pricing; optimization software defined networks; mobile edge computing; reinforcement learning; stochastic learning automata; game theory; data offloading; pricing; optimization
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Mitsis, G.; Apostolopoulos, P.A.; Tsiropoulou, E.E.; Papavassiliou, S. Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market. Future Internet 2019, 11, 118.

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