# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 5 Evaluation
### Experiment Results
#### 5.2.3 Impact of \(\xi\) and \(P_{max}\) on Performance of PARM  
From the analysis in Section 5.2.2, we find that in our design, PARM is effective in reducing the variation of the sites' congestion level, LDP is effective in helping telco CDN match revenue to cost by setting prices according to congestion level. In this part, we further explore the impact of key parameters (i.e., \(\xi\), \(P_{max}\)) on the effectiveness of PARM. **Impact of \(\xi\)**. From Figure 8 we have already observed that \(\xi\) is effective in bounding the variation. Here we observe the trend of different performance metrics when \(\xi\) varies. Define \(\xi\) as \(\tau\) percent of \(\overline{\theta}\), as shown by Equation (8). Figures 12-15 show the metric when \(\tau\) vary from 0.1 to 0.3. \[\xi=\tau.\overline{\theta} \tag{8}\]  
Figure 12 shows the trend of variation of the sites' congestion level. We observe that as \(\tau\) increases, the variation of four traffic patterns all increase. This is because a larger \(\tau\) enables a larger range for congestion level fluctuation among all sites when conducting request mapping. When \(\tau=0.1\), the variations are nearly the same. When \(\tau\) increases, the variation of the four values gets bigger and bigger. In addition, for any \(\tau\), the relationship between the variations in Traffic Pattern 4, Traffic Pattern 1, Traffic Pattern 3, Traffic Pattern 2 is consistent, i.e., from the largest to the smallest. This order conforms with the order of the overall congestion level of four traffic patterns. (Traffic Pattern 4 is the most congested and with the largest total bandwidth cost, Traffic Pattern 2 is the least congested and with the least total bandwidth cost, as shown in Figure 9) The larger the overall congestion level is, the larger the possible range for request mapping is, thus the numerical relationship holds. Figure 11: Comparison of application’s average latency. Figure 13 shows the trend of total bandwidth cost. First we observe that as \(\tau\) increases, the relationship between the total bandwidth cost in Traffic Pattern 4, Traffic Pattern 1, Traffic Pattern 3, Traffic Pattern 2 is consistent, i.e., from the largest to the smallest, conforming with the analysis in the previous paragraph. Specifically, when \(\tau\geq 0.15\), total bandwidth cost of Traffic Pattern 2 and Traffic Pattern 3 gradually increase. When \(\tau=0.3\), these two values reach 1.139 times and 1.123 times of the values when \(\tau=0.15\), respectively. For Traffic Pattern 1, the value nearly stay the same as \(\tau\) varies. For Traffic Pattern 4, the value stay nearly the same when \(\tau\) varies from 0.1 to 0.175, then it increase much at every step. When \(\tau=0.3\), the value reaches 1.202 times of the value when \(\tau=0.175\). Figure 14 shows the trend of total payment. First we observe that the value in Traffic Pattern 3 and Traffic Pattern 4 does not change as \(\tau\) increases. This is because in these two patterns, different CDN sites are with the same price, as shown in Figures 6 and 7, respectively. In comparison, the values in Traffic Pattern 1 and Traffic Pattern 2 both decrease as \(\tau\) increases. This is because in these two patterns, different CDN sites are with different prices. A larger \(\tau\) provides much more space for PARM to map requests to sites that are with lower prices. Note that the overall congestion level of Traffic Pattern 1 is higher than that of Traffic Pattern 2, thus the range of different prices in the former pattern  
Figure 12: Impact of \(\tau\) on sites’ variation in PARM. Figure 13: Impact of \(\tau\) on total bandwidth cost in PARM. is larger than that in the latter pattern. Therefore, the absolute value of gradient of the former pattern is bigger than that of the latter pattern. Figure 15 shows the trend of average latency. We observe that this metric is independent of \(\tau\). The average latency curves of Traffic Pattern 3 and Traffic Pattern 4 are more flat than those of the other two traffic patterns. **Impact of \(P_{max}\).