%0 Journal Article
%T Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
%J Future Internet | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/fi11050118
%X 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-Tex
%U https://www.mdpi.com/1999-5903/11/5/118