# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 1 Introduction
### Related Work  
The problem of data offloading from the end-users to the MEC servers for further computing has been extensively studied in the recent literature, while examining both the computation and the communication limitations [1]. In [2], a minimization problem of the long-term average weighted total devices' and MEC server's power consumption is formulated and solved in a multi-user MEC environment, concluding to a joint radio and computing resource management scheme, where both the optimal users' transmission power to offload their data and the corresponding computing power to process them are determined. In [3], a femto-based MEC environment is introduced and the authors exploit the trade-off between the end-users' energy consumption and latency towards minimizing the end-users' affordable latency while executing an application. A centralized optimization problem is introduced in [4] targeting at the minimization of the weighted sum end-users' energy consumption, while accounting for the end-users' computation latency constraints. The authors consider that the end-users adopt the orthogonal frequency division multiple access technique to offload their data to the MEC servers and they capture the end-users' workload offloading priorities in the problem formulation and solution, while a similar approach is also followed in [5]. In [6], the authors proposed a joint resource allocation scheme of the computation and communication resources of the MEC system aiming to minimize the end-users' energy consumption and the latency of the applications' execution at the MEC servers. Moreover, in [7], the authors focused on the energy efficient operation of the MEC system and they propose a dynamic data offloading and resource allocation scheme to minimize the computation application completion time and the end-users' energy consumption. A holistic framework of minimizing the total cost of energy, computation, and delay for the end-users is introduced in [8]. Game Theory has also been adopted to deal with the data offloading problem in the MEC environment, while providing the enhanced flexibility to the end-users to make autonomous data offloading decisions in a distributed manner [9]. In [10], a data offloading decision-making game is formulated among the end-users, who decide the amount of data that will be offloaded to a single MEC server, as well as the part of the computation task that will be executed locally at their devices. A similar problem is addressed in [11], while a multiple MEC servers environment is considered and the end-users have to additionally select to which MEC server they will offload part of their data. The problem of activating the MEC servers based on the end-users computing demands is addressed in [12], where the MEC servers' activation problem is formulated as a minority game and a distributed reinforcement learning algorithm is executed by each MEC server in order to determine if it will be active or not. The concept of applying usage-based pricing policies to the end-users while they exploit the MEC servers' computing capabilities is introduced in [13, 14] towards providing incentives to the end-users to consume the MEC servers' computing services in a fair manner. Recently, the capabilities of the SDN have been exploited by the MEC environment to efficiently and effectively deal with the data offloading problem, the activation of the MEC servers, the routing of the end-users offloading data, and the announcement of pricing mechanisms to control the smooth operation of the MEC system [15]. The problem of selecting a computing mode (i.e., local, MEC, or cloud computing) for each end-users' computation task is studied in [16], where the SDN controller executes the Computing Mode Selection algorithm and announces the corresponding routing policies to the end-users. The benefits of the combined use of SDN and MEC within the Internet of Things (IoT) systems are discussed in detail in the surveys [17; 18].