# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 3 Methodology
### Node Selection
The following equation explains the status of a possible process of choking:

\[6-Possible\ choking\ =\ \{\ 0\ \ if\ L_r\ \leq\ Com,\ \{\ 1\ if\ L_r\ >\ Com. \tag{6}\]

The equation presents Lr > Com in a binary situation if the total number of requests, Lr, is bigger than the capability of a serving data node, Com, such that the possibility of having constraint Com could be faced; otherwise, a constraint, Lc, does not seem possible. ### Performance metrics

This section explains the performance variations of key metrics explored to compare the advanced network architectures with traditional TCP/IP systems. Data Retrieval Time: This is the time consumed to fetch the data after a request is initiated. It indicates how efficiently data can be accessed but could be determined by factors like network topology, caching strategies, and routing protocols. Throughput: This assesses the volume of data transmitted via a network per unit of time. It is a very important indicator of how the network handles varying load conditions but also depends on the network capacity and efficiency. The Ratio of Cache Hits: The ratio of hits in the cache evaluates the efficacy of caching within a network and also reflects the portion of incoming requests served from the cache rather than the original source where the data was originally placed. Error Rates: Error rates can be defined as the occurrences of errors in data during transmission in a network. Lower error rates represent higher network performance. Resource Utilization: This metric helps to evaluate how efficiently a network uses its resources, such as network bandwidth, computation, and storage. Proper resource management is key to high performance and scalability, specifically in networks. Network Congestion: Network congestion occurs when the amount of data received exceeds the capacity of the available bandwidth of links in the network. It gives insights into real-time data traffic being received at links. Understanding congestion helps in evaluating the network's capacity to maintain performance. ### Performance Evaluations

The simulation methodology employed in this study utilized the NDN Simulator to comprehensively evaluate network performance under diverse operational conditions. The experimental environment was established on a Linux-based system running Ubuntu 20.04, leveraging the NS-3 network simulator integrated with ndnSIM for Named Data Networking implementations. The hardware configuration consisted of an Intel i7 8 th generation processor, 16 GB RAM, and 120 GB storage capacity, providing sufficient computational resources for extensive simulation campaigns. The evaluation framework incorporated three distinct network topologies to assess the generalizability and robustness of the proposed SARAC4N framework. These topologies included hierarchical structures that mimic enterprise or campus networks with tiered traffic patterns, grid configurations representative of structured environments such as smart grids or urban sensor networks, and random topologies that reflect the unpredictable nature of dynamic IoT and peer-to-peer systems. Each topology was configured with cache-enabled routers, consumer nodes, and producer nodes to simulate realistic content distribution scenarios. Network parameters were standardized across all experimental scenarios to ensure consistent comparative analysis. Link configurations maintained a 1% packet loss ratio, 10 ms delay, and 100 Mbps bandwidthcapacity, reflecting typical network conditions encountered in real-world deployments. These parameters provided a controlled environment for evaluating the performance characteristics of different caching strategies under comparable conditions. The simulation campaign encompassed multiple stress-testing scenarios designed to evaluate network resilience and adaptability. High-network-load conditions were systematically applied to assess the system's ability to maintain performance under increased traffic volumes while preserving acceptable delay characteristics.