# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 5 Evaluation
### Experiment Setup
#### 5.1.2 Performance Metrics and the Compared Cases  
We first observe the relationship between the CDN sites' congestion level and their prices under the LDP design. Then, we evaluate the performance of PARM by observing the following metrics:  
* The variation of the sites' congestion level;
* Bandwidth cost faced by the telco CDN. The bandwidth cost of a site \(m\) is calculated according to Equation (7) [47], where \(D_{m}\) is the actual traffic demand and \(C_{m}\) is the bandwidth capacity of the site;
* Total bandwidth payment of the application providers;
* Average latency perceived by end users. \[g(D_{m})=\left\{\begin{array}{cl}D_{m}&0\leq D_{m}/C_{m}<1/3\\ 3D_{m}-2/3C_{m}&1/3\leq D_{m}/C_{m}<2/3\\ 10D_{m}-16/3C_{m}&2/3\leq D_{m}/C_{m}<9/10\\ 70D_{m}-178/3C_{m}&9/10\leq D_{m}/C_{m}<1\\ 500D_{m}-1468/3C_{m}&1\leq D_{m}/C_{m}<11/10\\ 5000D_{m}-16318/3C_{m}&11/10\leq D_{m}/C_{m}<\infty.\end{array}\right.\] (7)  
The metrics above are observed in the three cases, which are compared in Table 4. In the first case, bandwidth usage is charged with a fixed price, and user requests are directed to the nearest CDN site. In the second case, bandwidth usage is charged with a fixed price, and request mapping rules are calculated based on optimization that only minimize the latency perceived by all user requests (i.e., the optimization is price-unware, set \(Psi=0\) in the PARM formulation). In the third case, bandwidth usage is charged with our LDP design, and request mapping rules are calculated by the PARM module. The above three are called **BASE**, **PURM**, and **PARM**, respectively. In Section 5.2.1, the congestion level in BASE is set as the historical congestion level. In Sections 5.2.2 and 5.2.3, the congestion level in BASE is set as the initial historical congestion level and the congestion level of the previous PARM period is always set as the historical congestion level of the upcoming PARM period.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **User Numbers in Every Region** \\ \hline Traffic Pattern 1 & [110, 150, 90, 230, 320, 190, 430, 380, 450, 500] \\ Traffic Pattern 2 & [90, 255, 375, 160, 80, 150, 310, 75, 150, 175] \\ Traffic Pattern 3 & [230, 210, 190, 240, 220, 200, 205, 225, 245, 215] \\ Traffic Pattern 4 & [300, 320, 305, 310, 325, 330, 315, 290, 305, 325] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic patterns in the experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(M\) & 10 \\ \(L\) & 10 \\ \(N\) & 3 \\ \(t_{n}\) & [90 ms, 70 ms, 60 ms] \\ \(\lambda_{mu}\) & [61.92, 39.84, 22.08] \\ \(P_{const}\) & 0.3 \\ \(P_{min}\) & 0.2 \\ \(P_{max}\) & 0.5 \\ \(\Psi\) & 0.5 \\ \(\sigma_{1},\sigma_{2}\) & \(\sigma_{11}=0.3,\sigma_{12}=0.7,\sigma_{21}=0.4,\sigma_{22}=1.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters’ setting in the experiment.  
In addition, in Section 5.2.3, we observe the impact of \(\xi\) and \(P_{max}\) on performance metrics of PARM. (In Section 5.2.2, \(\xi\) is set as 0.15\(\hat{\theta}\) and \(P_{max}\) is set as 0.5)