# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 6 Results
### Operation of the DO-MECS Framework
#### 6.1.1 Homogeneous End-Users  
Initially, with respect to the scenario of homogeneous end-users, Figure 3 presents, in a comprehensive manner, indicative numerical results regarding the pure operation of the DO-MECS algorithm, in order to gain some insight about the key operational characteristics and contributions of the various components of our framework. We considered a simplistic demonstration scenario, which however did not harm the validity of the observations. To the contrary, it was selected such that we verified the operational characteristics of our proposed approach, where each user's maximum \begin{table}
\begin{tabular}{l c c} \hline \hline
**Server** & **Cost \(c\)** & **Discount \(f_{s}\)** \\ \hline server 1 & 0.12 & 0.05 \\ server 2 & 0.14 & 0.04 \\ server 3 & 0.20 & 0.02 \\ server 4 & 0.17 & 0.03 \\ server 5 & 0.13 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MEC servers’ characteristics. amount of data was the same \(I_{u}^{(t)}=1000\) Bytes. We did not consider or differentiate them based on the nature of the executed tasks or on parameters related to the computing or data intensity. It is also stressed that the focus of this paper and of the corresponding evaluation results is on the decision making process of the data offloading (i.e., server selection and part of data to be offloaded), and not on the actual offloading and/or computation processing itself. Specifically, Figure 3a presents the relative pricing of each MEC server, i.e., \(\frac{\sum_{k\
eq i}(1-f_{k}^{(t)})\eta_{k}^{(t)}}{(1-f_{k}^{(t)})\eta_{k}^{(t)}}\), as it was determined at the end of each time slot with respect to the time slots that the DO-MECS algorithm needs to converge. It was observed that in all cases convergence was obtained in fewer than 3000 time slots, while for practical purposes fewer than 2000 time slots were sufficient, corresponding to actual running time of less than 14 s for learning rate \(b=0.2\). Note that significantly lower convergence times could be achieved if higher learning rates were considered, as demonstrated below in Section 6.2.1. Note that the times measured and reported here refer to the convergence of the overall DO-MECS algorithm in our simulation (i.e., decision making process), where the users conclude to a stable selection of MEC servers in order to offload their data to be further processed. As presented in Figure 3a,b, the greater was the relative pricing for each MEC server, the more attractive it became for the end-users. Server 1 clearly accumulated the majority of the end-users since in Table 1 we notice that Server 1 had both the smallest cost and offered the highest discount compared to the other MEC servers. The same trend and reasoning followed for the rest of the servers. Please note here that, due to the homogeneity of the considered population, each end-user offloaded the same amount of data (in this experiment offloaded its total data, i.e., \(I_{u}^{(t)}=1000\) Bytes), to the corresponding selected MEC server, as determined by the MEC Server Selection process (Step 2 of DO-MECS Algorithm) based on the theory of the stochastic learning automata (Section 3). In Section 6.1.2, a different scenario with heterogeneous end-users was considered and demonstrated, where the end-users decided to offload different amounts of data, based on the overall system dynamics. Figure 3: _Cont._  
As expected, the congestion on each MEC server, i.e., \((1+CONG_{s})^{3}\), followed the same trend as the number of end-users selecting each MEC server (Figure 3b). The latter observation was expected, as the more end-users selected to offload their data to a MEC server, the more congested that MEC server became (Figure 3c) and a greater penetration, i.e., \(\frac{\sum_{t\in(1\dots T)}\sum_{u\in U}b_{u}^{(t)}}{\sum_{u\in S}\sum_{t\in(1 \dots T)}\sum_{u\in U}b_{u}^{(t)}}\), was achieved by that server. In particular, the MEC servers' penetration in serving the end-users computing demands is presented in Figure 3d.