## 7 Performance Evaluation
### Main Reachable Results
If \(N_{NSIS-MIP}(ho)\) is the overall number of NSIS-MIP users who have made at least ho hand-overs from the first cell and \(n_{NSIS-MIP}(ho)\) is the overall number of NSIS-MIP users who did not find a passive reservation after the ho-th hand-off event, then the prediction error on \(ho\)-th hand-off event is \(e(ho)=n_{NSIS-MIP}(ho)/N_{NSIS-MIP}(ho)\). In addition, for a fixed prediction scheme with a fixed set of input parameters the trend is almost constant if the NSIS-MIP traffic percentage is varied; obviously, the prediction algorithm is not affected by the number of admitted flows. Some input combinations must be excluded, because they do not lead to any acceptable result. The best results were obtained for the dynamic case with \(\delta=0.5\) and they are not comparable with those obtained with any other input values of the dynamic scheme or with the static one, which has good performances for the input sequence \(C_{i1}=1\), \(C_{i2}=2\), \(C_{i3}=3\). The minimum and maximum error for \(\delta=0.5\) are 10% and 12.57%. Figure 12: Average number of NSIS-MIP dropped flows vs NSIS-MIP traffic percentage. Table 2 shows the average percentage of NSIS-MDP users that suffered an outage event during their active sessions (NSIS-MIP analysis is not shown because the outage percentage resulted less than 0.1% for every combination of scheme/parameters): in our simulations the outage threshold was fixed at \(p_{outage}\) = 0.05. The implemented CAC and bandwidth allocation algorithm ensure the threshold constraint to be respected: in fact, the maximum observed value of outage percentage for NSIS-MDP users is 0.0502%. In particular, as discussed earlier, NSIS-MIP users are privileged when compared to NSIS-MDP ones, because of the guaranteed service continuity and the passive reservation policy. In addition the bandwidth allocation algorithm privileges NSIS-MIP users when choosing a benefactor who has to give up a portion of his allocated resources (which is why the percentage increases for higher NSIS-MIP traffic). In terms of outage percentage of NSIS-MDP flows (also for NSIS-MIP ones) no large differences can be observed between static and dynamic schemes. The increasing trend of the values in Table 2 does not depend on the adopted prediction policy, but is due to the higher presence of NSIS-MIP flows, which have a certain allocation priority. From the figures above it can be concluded that both static and dynamic prediction schemes are able to guarantee a good level of QoS for different input parameters; in particular, the static scheme is able to ensure a slightly higher level of utility (Figure 8) with an acceptable system utilization (Figure 9). The percentage of dropped NSIS-MIP flows is also maintained under the value of 1%. The best obtained input sequence for the static scheme is \(C_{i1}\) = 1, \(C_{i2}\) = 2, \(C_{i3}\) = 3; this can be explained by considering the increasing in prediction error for higher hand-off events  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**STATIC**} & \multicolumn{8}{c}{**DYNAMIC**} \\ \hline
**NSIS-MIP** & **111** & **123** & **321** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** \\ \hline
0 & 0.01213 & 0.01275 & 0.01542 & 0.01547 & 0.01551  
due to the intrinsic error that was committed in the generic previous step. Pre-reserving resources on a higher number of cells for the next hand-off event can balance previous prediction errors. The dynamic scheme offers slightly lower performances in terms of amount of assigned bandwidth and perceived utility, but it outperforms the static one in terms of prediction error. For the first hand-off only the static sequence \(C_{i1}=3\), \(C_{i2}=2\), \(C_{i3}=1\) leads to a negligible value of \(e(1)\), because reserving on \(C_{i1}=3\) cells reduces the probability of error near to zero. However, the dynamic threshold-based algorithm performs better in the \"long-range\" prediction: the maximum value of \(e(3)\) is 12.57% for \(\delta=0.5\).