## 6 Simulation Results and Discussions
### ReLoss-TCC-Based RWTA Strategy in Different Scenarios  
In this section, we try to find the effect of TCC on the performance of blocking probability under ReLoss-TCC-based RWTA strategy in QKD optical networks. Two kinds of TCC, i.e., average TCC and sampled TCC, are introduced in this part. Average TCC is defined as the average value of TCC calculated right after accommodating each request in the simulation, while sampled TCC refers to the average value of TCC calculated by sampling network wavelength-timeslots status in range \([t_{a},\ t_{a}+t_{0}]\) after accommodating each request. Here, \(t_{a}\) is the arrival time of request and \(t_{0}\) is a constant corresponding to the sample strategy. The main difference between them is that for sampled TCC, services which are accommodated in the network may come to an end during \([t_{a},\ t_{a}+t_{0}]\) and thus release the wavelength-timeslot resources, resulting in a different value of TCC. In this section, four wavelengths are assigned for QKCh and MBCh respectively, and the number of required timeslots is randomly generated among [5,15] timeslots. In addition, \(TW_{u}\) is set to be 3 timeslots, and \(t_{0}\) is set to be 10 timeslots.  
Figure 5(a) indicates the relationship among average TCC, sampled TCC, and blocking probability. From these curves we can see that, as the traffic load increased, the sampled TCC decreased uniformly, and the blocking probability increased uniformly. However, for average TCC, it dropped significantly when the traffic load increased lightly and remained relatively low when the traffic load continued to increase. Then, we evaluate the sampled TCC for three different RWTA strategies, i.e., ReLoss-TCC-based RWTA strategy, random-fit (RF)-based RWTA strategy and first-fit (FF)-based RWTA strategy under different traffic loads, as shown in Figure 5(b). RF-based RWTA strategy refers to the random selection among all alternative wavelength-timeslot segments, while FF-based RWTA strategy selects the available wavelength-timeslot segments with the lowest index. From the results we can clearly concludes that among these three candidates, ReLoss-TCC-based RWTA strategy achieved the best performance, followed by FF-based RWTA strategy, and lastly RF-based RWTA strategy.  
To further verify the relationship between sampled TCC and provisioning capability of the network, we display the blocking probabilities of ReLoss-TCC-based RWTA strategy, RF-based RWTA strategy, and FF-based RWTA strategy under different traffic loads, as shown in Figure 6(a). Combined with Figure 5(b), we can see that higher value of sampled TCC will achieve lower blocking probability in the network, and vice versa. This confirms the previous conclusion we got from Figure 5(a). Moreover, in order to explore the performance of RWTA strategy under different scenarios, two kinds of timeslot requirements were  
Figure 6: (a) Relationship among average TCC, sampled TCC, and blocking probability; (b) Sampled TCC under ReLoss-TCC-based RWTA, RF-based RWTA, and FF-based RWTA strategy.  
configured for key requests. One is the requirements of requests are diverse, and they are randomly chosen from [5,15] timeslots; another is the requirements are constant, and they are fixed to 10 timeslots. For the former scenario, ReLoss-TCC-based RWTA strategy got the lowest blocking probability, and the gaps between different strategies were obvious. For example, when the traffic load is 160 Erlang, the ReLoss-TCC-based RWTA strategy can obtain 29.69% and 20.35% reduction compared to RF-based RWTA strategy and FF-based RWTA strategy respectively, as shown in Figure 7a. However, for the later scenario, the gaps in blocking probabilities of those three strategies were not prominent, with ReLoss-TCC-based RWTA strategy performing slightly better under most of the traffic loads, as shown in Figure 7b. This indicates that the proposed ReLoss-TCC-based RWTA strategy is more applicable to the scenarios with diverse timeslots requirements.