## 3 Simulation Model
### Second Stage--Routing Path Application  
The functional architecture of our OBS model has similar characteristics to the ones presented in [3, 60], assuming, like it is done in these proposals, that each node can support both new input traffic generated by the client networks and the _in transitu_ traffic passing all-optically from source to final destination. The simulation studies were done on different networks, including two from North America (ARPANET and NSFNET) one European (COST239) and one with a random configuration of nodes, whose topologies are depicted in Figure 7. Some physical parameters of these networks are presented in Table 1, in which the average degree is considered the average number of physical connections per node, and the physical connectivity is defined as the normalized number of bidirectional links with respect to a physically fully connected network of the same size [61]. The nodes are connected by links representing optical fibers with the same characteristics, _i.e._, having 16 wavelengths per link with a transmission capacity of 10 gigabits per second (per wavelength). This simulation model was conceived to work in a stochastic self-driven way. Thus, it generates its own input data traffic, representing the load offered to the OBS network to be switched and forwarded. Since in this model we are mainly interested in a comparative evaluation of alternative routing strategies, the issue of packet aggregation and burst assembly is not considered. This means that the traffic used in the network is already burst based traffic after the assembly process. Figure 6: Conceptual view of the simulation methodology. **Traffic pattern:** The traffic characteristics of an OBS network are highly dependent on the burst assembly algorithm in use [60, 62, 63]. Both theoretical and simulation results show a _smoothing effect_ resultant from a reduction in the degree of self-similarity of the input packeted traffic. In [63] it is argued that, despite of long range dependence, the burst arrival process can be assumed to be Poisson in low timescales. As a consequence, there is nearly no influence of self-similarity on blocking probability, while the influence is significant in optical buffers dimensioning. Therefore, in our approach, the adopted traffic pattern is based on a Poisson arrival pattern assuming a previous burst length threshold assembly method, generating messages that are \(100\times 10^{3}\) bytes. **Signaling scheme:** The bursts are forwarded through the core backbone reproducing the relevant actions of the JET signaling scheme, _i.e._, using delayed reservation and implicit release. The CP processing time is assumed to be 10 \(\mu\)s and average switching time in a core node is also 10 \(\mu\)s, although other values from 12.5 \(\mu\)s down to 1 \(\mu\)s could be adopted depending on the technology in place (current state-of-art or foreseeable in the near future). The timings assumed by this configuration parameters are in agreement to the ones widely used in OBS research, like in [64, 65], for example. \begin{table}
\begin{tabular}{c c c c c c} \hline Network & Num of nodes & Num of links & Degree (av) & Connectivity (stdev) & \\ \hline ARPANET & 20 & 62 & 3.1 & 0.45 & 0.16 \\ NSFNET & 14 & 42 & 3.0 & 0.55 & 0.23 \\ RANDOM12 & 12 & 42 & 3.5 & 0.67 & 0.32 \\ COST239 & 11 & 52 & 4.7 & 0.65 & 0.47 \\ \hline \end{tabular}
\end{table} 
Table 1: Network physical parameters. Figure 7: Network topologies under study. **Routing:** The model employs source routing in which a complete routing decision is taken at the ingress edge node. Like the approach adopted in [10; 54], the path over which the burst must travel is carried by the CP that precedes the transmission of each burst and is not modified by downstream nodes. The adopted path is fetched from the edge nodes routing tables, previously populated by the results of the path selection strategies calculated during the \(1^{st}\) stage of this model.