# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 1 Introduction
### Contributions and Outline  
This paper aims at filling the aforementioned research gaps by proposing an SDN-powered MEC architecture towards dealing with the joint problem of intelligent MEC server selection and end-users' data offloading in a multiple MEC server and multiple end-user environment. From an architectural point of view, the joint optimization problem is solved at the SDN controller, which announces the best strategies to the MEC servers and the end-users. The goal of the MEC servers is to maximize their profit by serving the end-users' computing demands, while the end-users aim at maximizing their perceived satisfaction (expressed through representative utility functions) by the provided computing services from the MEC environment.  
The key contributions of our work that differentiate it from the rest of the literature are summarized as follows.  
1. The monetary-based pricing of the MEC servers' computing services, the offered discount to the end-users, the total end-users' offloaded workload, and the cost of the MEC servers to process the workload are considered towards formulating representative welfare functions for the MEC servers, creating a multi-server competitive computing market. In addition, the end-users' perceived satisfaction from executing their tasks to the MEC servers is captured in holistic utility functions, while considering the corresponding cost that the end-users have to pay in order to enjoy the requested services (Section 2).
2. A reinforcement learning framework is included within the SDN controller's functionalities towards implementing the MEC server selection by the end-users to offload their data for further processing. The theory of stochastic learning automata is adopted, where the end-users are represented as stochastic agents at the SDN controller, learning the best MEC server selection based on their previous actions and the reaction of the MEC environment. Each MEC server is characterized by a reputation score, which acts as a reward probability to the MEC server selection process. The reputation score captures the discount offered by the MEC server, its congestion in terms of serving end-users' computing tasks, its penetration in terms of serving the end-users' computing demands, and its announced pricing for its computing services (Section 3).
3. Following the reinforcement learning-based MEC server selection by the end-users, a two-layer optimization problem is formulated and solved aiming at maximizing the MEC servers' profit and also maximizing the perceived satisfaction by the end-users. The end-users' maximization problem of their satisfaction is addressed at the first stage as a non-cooperative game among the end-users, who practically aim at maximizing their personal utility function. A Nash Equilibrium (NE) point is determined, which expresses the optimal amount of offloading data for each end-user. At the second stage of the joint optimization problem, given the end-users' offloadeddata, an optimization problem of each MEC server's profit (as expressed by its welfare function) is formulated and solved by the MEC servers (Section 4).
4. An iterative and low-complexity algorithm is introduced to implement the MEC server selection process based on reinforcement learning and determine the optimal MEC servers' computing services' monetary pricing and end-users' optimal data offloading based on game-theoretic and optimization techniques (Section 5).
5. A series of detailed simulation experiments are performed to evaluate the performance and inherent attributes of the proposed framework. Furthermore, a comparative study demonstrates its superiority and benefits compared to other relevant alternative strategies (Section 6). Finally, Section 7 concludes the paper.