# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 5 Experimental Setup and Results
### Simulation Results  
In order to evaluate the performance of proposed approach, results are compared with a reactive caching approach, in which files are stored in the cache if they were repeatedly requested in the past based on the history of files accessed. Initially, a system model is generated using a preferential attachment model [17], and uniform distribution is considered for user requests' generation. Based on the parameters setup shown in Table 2, the proposed algorithm is evaluated for a different number of requests and cache size. In Figure 5, we plot satisfaction rate with respect to the number of requests generated by the users and the user's cache size. Our experimental tests are based on number of active users in the network. We considered two main scenarios, first high load scenario when 100% users are active i.e., 256 users and second, and a low load scenario when 50% of the users are active i.e., 128 users. In Figure 5 (left), we measured satisfied requests of the proposed approach based on proactive caching with low and high load against reactive caching with respect to the number of requests. We see that the performance of reactive caching decreases more as compared with proactive caching when the number of requests increases. We see that with 100% requests, the number of satisfied requests is about 48%. We also note that, with 50% of requests, the proposed approach satisfies 52% requests on average with low load and satisfies 45% requests with high load, whereas these are 36% and 31%, respectively, in the case of the reactive approach. In Figure 5 (right), the effect of cache size on satisfaction rate is shown. We can see that with caching 20% of files, 65% of requests are satisfied by the proposed approach while caching 80% files increases the satisfied requests to 100% with low load, whereas these are 30% and 100%, respectively, in the case of reactive caching. In Figure 6, we plot backhaul traffic load, with respect to the number of requests generated by the users and also with respect to the user's cache size. It can be seen from Figure 6 (left) that, as we increase the number of requests, the backhaul traffic also increases. High load scenarios cause more backhaul traffic to increase as compared to low load scenarios. With the proposed approach, we see that on 100% requests, the backhaul load is 52% on low load and 65% on high load, whereas these are 75% and 79%, respectively, in the reactive approach. The proposed approach thus obtains significant reduction in backhaul traffic load as compared with the reactive caching approach. The effect of cache size on backhaul traffic is shown in Figure 6 (right). We see that with low values of cache size, the backhaul traffic load is significantly lower in the case of the proposed approach as compared \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**(a) Genetic Algorithm Parameters**} \\ \hline
**Parameter** & **Description** & **Value** \\ \hline \(P_{n}\) & Population size & 500 \\ \(P_{m}\) & Mutation rate & 0.2 \\ \(P_{s}\) & Crossover rate & 0.2 \\ \(T_{c}\) & Selection criteria & 0.1 \\ \(\alpha\) & Mutation extension rate & 0.02 \\ \(r\) & Termination criteria & 50 \\ \hline \multicolumn{3}{c}{**(b) Proposed Approach Parameters**} \\ \hline
**Parameter** & **Description** & **Value** \\ \hline K & Total of communities & 15 \\ N & Users & 256 \\ \(|F|\) & Total number of files & 1024 \\ \(l_{i}\) & Length of each file & 1 Mbits \\ \(b_{i}\) & Bitrate of each file & 1 Mbits/s \\ \(C_{up}\) & Total small base link capacity & 64 Mbits/s \\ \(C_{b}\) & Total D2D link capacity & 128 Mbits/s \\ R & Maximum number of requests & 10,000 \\ D & Total D2D cache size & 256 Mbits \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. to the reactive approach. For instance, with cache size of 10%, the backhaul traffic load is more than 90% in the case of reactive caching, whereas it is half of this in the proposed approach. As we increase cache size of the influential user, backhaul traffic decreases.