## 4 Results and Discussion
### Discussion  
The presented results allow drawing synthetic conclusions on how modifying the IMS/NGN network and traffic source parameters in domain 1 affects all analyzed CPP parameters:  
* The intensities of requests significantly affect \(E(CSD)\) and \(E(CSD)\) times:
* Registration and intra-operator call set-up requests generated in domain 1 (**lambda*
* and **lambda1d**) increase only CPP parameters for scenarios performed in domain 1 (b1, d1, f1 and f2);
* Inter-operator call set-up requests generated in domain 1 (**lambda2d**) increase all investigated CPP parameters. * The processing/response times of network elements increase \(E(CSD)\) and \(E(CSD)\) times dependent on the type of network element and its usage in particular call scenarios:
* Changing the times of processing SIP INVITE messages by CSCF servers in domain 1 (**TINV**) and the mean time of processing requests by RACF A1 (**EXA**) affects CPP parameters for all call scenarios performed in domain 1 (b1, d1, f1 and f2);
* Modification of the message processing times of RACF C1 (**TA**, **Tproc*
* and **Tresp**) and the mean response time of \"MPLS core 1\" (**ETR**) affects only CPP parameters for call scenarios with resource reservation in the core network of domain 1 (d1, f1 and f2);
* Changing the mean time of processing requests by SUP-FE 1/SAA-FE 1 (**EY**) influences only \(E(CSD)_{\text{f2}}\), as this network element is used solely in establishing inter-operator calls originated in domain 2. * Link parameters in domain 1 affect \(E(CSD)\) and \(E(CSD)\) times for all call scenarios performed in domain 1 (b1, d1, f1 and f2):
* Link lengths (**d**) have an influence on propagation delays and increase the analyzed CPP parameters linearly;
* Sufficient link bandwidths (**b**) must be assured, as below a certain bandwidth level, \(E(CSD)\) and \(E(CSD)\) times rise rapidly. * Other parameters of our traffic model (**rC**, **pb**, **p11** and **p21**) do not have a significant impact on the analyzed CPP parameters. Based on the abovementioned remarks, it is possible to indicate the influence of the IMS/NGN network and traffic source parameters in domain 2 on the analyzed \(E(CSD)\) and \(E(CDD)\) times using the rule of symmetry. For example, larger values of **lambdaR** and **lambda1d** in domain 1 increase the analyzed CPP parameters for scenarios b1, d1, f1 and f2. Similar changes in **lambdaR** and **lambda1d** values in domain 2 will affect the \(E(CSD)\) and \(E(CDD)\) times for scenarios b2, d2, f2 and f1. As a result of identifying the relations between the input and output parameters of our traffic model, the performed research (Table 3, Figures 4-11) allows grouping the parameters of the IMS/NGN network with an MPLS-based transport stratum into three categories, which correspond to their importance for the network operator and designer. The first group contains input variables with a significant impact on CPP parameters, which must definitely be taken into account during network analysis and design. This group includes most of the input variables of our model: **lambdaR**, **lambda1d**, **lambda2d**, **TINV**, **TA**, **Tproc**, **Tresp**, **EXA**, **ETR**, **d** and **b**. The second group consists of network parameters that generally have little effect on \(E(CSD)\) and \(E(CDD)\) times: **EY**, **p11** and **p21**. The **EY** input variable affects only one CPP parameter of the twelve analyzed, while the **p11** and **p21** probabilities have a very limited impact on several output parameters. The third group includes input parameters with a negligible influence on mean \(CSD\) and \(CDD\) times: **rC** and **pb**. It is worth noting that from the group of input variables that most affect \(E(CSD)\) and \(E(CDD)\) times, mainly the request intensities (**lambdaR**, **lambda1d** and **lambda2d**), which have a direct impact on the offered traffic load, change during normal operation of the IMS/NGN network.