# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 4 Simulation  
In the process of evaluating the performance of our proposed routing approach, we conducted a comprehensive series of experiments with the primary objective of assessing its effectiveness in terms of success ratio, message processing overhead, and network cost. In the subsequent discussion, we will delve into the details of the experimental design and methodologies employed to achieve these objectives. \[\textit{Success Ratio}=\frac{\textit{Number of New Members Accepted}}{\textit{Total Number of Join Requests}} \tag{10}\]  
\[\textit{Message Processing Overhead}=\frac{\textit{Total Number of Message Processing}}{\textit{Total Number of Join Requests}} \tag{11}\]  
\[\textit{Network Cost}=\frac{\textit{Total Cost}}{\textit{Total Number of Join Requests}} \tag{12}\]  
The success ratio, denoted by the ratio of the total number of successful members to the overall number of requests, is mathematically expressed in Formula (10). Message processing overhead, as quantified in Formula (11), is characterized by the average number of message processing times during the construction of a route for a join request. This definition implies that if a message is processed n times during route construction, its overhead is considered as n. The network cost is defined as the average cost per receiver, with the Total Cost in Equation (12) representing the sum of the Tree Costs of all multicast trees in the network. In addition, our algorithm was compared with the PIM-SM routing protocol, Jia's Algo-based routing protocol, and QMRP. In the case of PIM-SM, the source node was designated as the tree root. Jia's Algo, a source-driven routing approach, initiates a search for potential optimal paths within nodes of the multicast tree starting from the source. Upon reaching the leaf node, the results are consolidated and returned to the source, which then determines the optimal path and communicates this information to the leaf nodes, instructing them to construct paths to the receiver nodes. QMRP, as a receiver-driven routing algorithm, QMRP adheres to the recommended parameters, with the maximum fallback limit set at 2. In scenarios where the QoS requirements of the request cannot be met during the tree-building process, ours algorithm will continue to construct the multicast tree along the path with the minimum delay; PIM-SM will continue building the multicast tree along the unicast routing; [PERSON]'s algorithm will proceed by selecting the minimum delay tree node to build the path to the receiver and QMRP will terminate the routing construction process. We implemented the aforementioned algorithms in NS-3 [30], a discrete-event simulation platform. We used the [PERSON] [13] (generalized linear preference) model to generate network topologies in our simulation experiments. The [PERSON] model, proposed by [PERSON] in 1988, is a classic random graph topology model. Nodes are positioned randomly on a rectangular coordinate grid, with each node located at integer coordinates. The likelihood of establishing a connection between any two nodes is contingent on their distance, as defined below:  
\[P(u,v)=\alpha\cdot\exp\left(-\frac{d}{\beta L}\right) \tag{13}\]  
The parameters \(\alpha\) and \(\beta\) take values within the range (0,1]. Here, \(d\) represents the Euclidean distance between node \(u\) and \(v\), and \(L\) signifies the maximum distance within the graph. A higher value of \(\beta\) results in a denser network with more edges, whereas a lower value of \(\alpha\) leads to a higher density of short edges. We generated 15 topologies, each consisting of 200 nodes, using the same parameters. The specific parameters are as follows: \(\alpha~{}=~{}0.063,~{}\beta~{}=~{}2.2,~{}L~{}=~{}10\),000. We employed a consistent random number seed across all sets of comparative experiments with identical parameters to minimize the interference of random processes on the experimental results.