# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology
### Node Selection
### Performance

The \"CPU Utilization\" graph in Figure 8 presents the percentage of computation against data requests with four caching mechanisms. The SARAC4N mechanism outperformed its counterparts with a very obvious margin with lesser CPU utilization. MCD and LCD took more computation to process the incoming requests, while CCndnS performed a little better. Figure 8: PU utilization comparison across caching schemes (SARAC4N, CCndnS, LCD, and MCD) with increasing request load. CPU: Central Processing Unit. CCndnS: Centralized Cooperative Named Data Scheme. Figure 7: Data retrieval time comparison across caching mechanisms. The simulation was conducted using ndnSIM on NS-3. Each network topology was simulated with 50 nodes (including 40 consumers and 10 producers), and each content file was set at a uniform size of 1 MB. Content request rates were generated following a Zipf distribution with parameter \(\alpha~{}=~{}1.2\), representing skewed user demand. The simulations ran for 1000 s per scenario, and performance was tested under increasing node counts (50, 100, and 150 nodes) to evaluate scalability. This presentation identifies that the other schemes might not be as effective as SARAC4N due to their straightforward handling mechanisms. They lack the dynamic feature of managing resources. Figure 8 shows the PU utilization comparison across caching schemes (SARAC4N, CCndnS, LCD, and MCD) with increasing request load. CPU: Central Processing Unit. CCndnS: Centralized Cooperative Named Data Scheme. ### Memory Utilization

The \"Memory Utilization\" graph in Figure 9 presents the direct association of memory utilization and the quantity of received data requests. MCD, LCD, CCndnS, and SARAC4N were compared with the same thresholds. SARAC4N's segmentation mechanism accomplishes efficiency by caching the most popular and socially aware content. All strategies are far behind in comparison with SARAC4N. MCD stays at 60% utilization consistently, LCD varies between 40% and 65%, and CCndnS stays at 60%, while SARAC4N starts at 20% and stabilizes at 40%. The graph shows that SARAC4N's strategy of creating clusters on the bases of measuring the social metrics and storing the content on the same basis in neighboring nodes proves its responsiveness along with lesser utilization of resources. ### Error Rate

The error rate graphs in Figure 10 present four trials against different methodologies. SARAC4N is compared with CCndnS, LCD, and MCD. It consistently shows reduced data choking points, suggesting that it successfully manages volumes of data with higher efficacy. This aligns with the cluster-based approach highlighted in previous research, where SARAC4N likely incorporates socially aware caching to optimize data routes and minimize bottlenecks. LCD, on the other hand, exhibits a notable result in the third trial, indicating that it is less adaptable than SARAC4N when it comes to handling sudden spikes in network demand. Figure 9: Memory utilization (%) across varying request loads. SARAC4N shows consistently lower usage compared to CCndnS (Content-Centric Named Data Networking with Social Awareness), LCD (Least-Cost Data), and MCD (Most Centralized Data). SARAC4N demonstrates low and stable error rates, especially compared to MCD and LCD, which show significant fluctuations across the trials. This consistent performance by SARAC4N may be due to its robust clustering and caching strategies due to social factors and ideal network positioning. By these means, SARAC4N reduces errors stemming from inaccurate predictions or misallocated cache storage. ### Probability of Data Finding

The \"Probability of Cache Hit\" graph in Figure 11 presents the likelihood of retrieving content from the cache of the nearby or local node. SARAC4N stands out with a very high cache hit probability and remains stable due to its social awareness capability, while MCD and CCndnS rank lowest. Socialness has proved to be very critical in cache-based decisions for the relevance of content.