SP Optimisation Results for GEANT Network

In this section, the SP optimised results for MLU, ETED, and throughput metrics are presented and compared with the results from Section 6.6.1.

6.6.3.1 Maximum Link Utilisation

Figure 6.18 shows the MLU across all network links. MLU in GA-SP is 6.7% lower than MLU in original SP. This shows a further 0.2% reduction compared to 6.5% MLU reduction case for SCIR optimisation (Figure 6.9). In addition, the original SCIR achieves maximum 12.7% MLU reduction compared to original SP. This is less than the 16.2% MLU reduction for the same metric in SCIR optimisation (Figure 6.9). Optimised SCIR achieves maximum 15.5% MLU reduction compared to original SP and 8.8% compared to optimised SP (original SP MLU being the base for difference percentage calculation). This is also less than the respective values of 21% and 14.5% MLU reduc- tion for the same metrics for optimised SCIR in SCIR optimisation scenario (Figure 6.9). A further observation is the optimised SCIR MLU stays well below the original SCIR MLU across the entire range of QoS-Sensitive traffic ratio.

Figure 6.18. Maximum Link Utilisation

6.6.3.2 Mean End-to-End Path Delay

Figure 6.19 shows the mean ETE path delay across all network links. ETED in optimised SP is 40.5% lower than ETED in original SP. This shows a further 2.1% reduction compared to 38.4% ETED reduction case for SCIR optimisation (Figure 6.10). In addition, the original SCIR achieves maximum 34.7% ETED reduction compared to original SP. This is less than the 39.9% ETED re- duction for the same metric in SCIR optimisation (Figure 6.10). Optimised SCIR achieves maximum 52% ETED reduction compared to original SP and 11.5% compared to optimised SP (original SP MLU being the base for difference percentage calculation). This is also less than the respective values of 54.1% and 15.7% ETED reduction for the same metrics for optimised SCIR in SCIR optimisation scenario (Figure 6.10). A further observation is the optimised SCIR ETED stays well below the original SCIR ETED across the entire range of QoS-Sensitive traffic ratio.

6.6.3.3 Throughput

Figure 6.20 shows the throughput across all network links. Throughput in optimised SP is 3.1% higher than throughput in original SP. This is the same as the 3.1% throughput increase case for SCIR optimisation (Figure 6.11). In addition, the original SCIR achieves maximum 2.6% through- put increase compared to original SP. This is less than the 3.1% throughput increase for the same metric in SCIR optimisation (Figure 6.11). Optimised SCIR achieves maximum 3.2% throughput increase compared to original SP and 0.1% compared to optimised SP (original SP MLU being the base for difference percentage calculation). This is also less than the respective values of 3.4% and

0.3% throughput increase for the same metrics for optimised SCIR in SCIR optimisation scenario (Figure 6.11). A further observation is the optimised SCIR throughput stays well above the original SCIR throughput across the entire range of QoS-Sensitive traffic ratio.

Figure 6.19. Mean End-to-End Path Delay

6.7

Discussion of Results

- Figures 6.7 and 6.13 show that SCIR has a higher mean link utilisation in cooperative rout- ing compared to SP, both in original link weight setting and optimised link weight setting. As explained in Section 5.4.1.1, the reason is the alternative paths in SCIR in general have more links and therefore tend to increase the overall link utilisation.

- The higher mean link utilisation in optimised link weight setting compared to the original link weight setting has similar reason to the above. It was demonstrated that GA has found a set of link weights which gives lower levels of MLU. This means that the GA- SP and GA- SCIR paths are typically longer but have the ability the spread out the traffic more evenly. Therefore, the average utilisation of network links increases because of longer paths that in- volve more number of links to route the traffic between sources and destinations.

- The lower standard deviation of link utilisation for SCIR compared to SP is shown in Fig- ures 6.8 and 6.14 for original link weights and optimised link weights. This means the traffic is more evenly balanced, and shows the superiority of SCIR over SP in this respect. Although this improvement comes at the expense of higher average traffic in the network ( Figures 6.7 and 6.13), SCIR can provide better traffic balancing than SP across a wide range of QoS-Sensitive traffic fractions.

- Figures 6.9 and 6.15 show the accomplishment of SCIR in meeting the ISP’s performance objective of minimising the MLU. The MLU in both scenarios (original link weights and optimised link weights) has decreased, since part of the QoS-Sensitive traffic is routed through non-shortest paths and this ultimately reduces the load on the most heavily loaded links.

- Figures 6.10 and 6.16 demonstrate the fulfilment of the end-user objective of minimising the mean ETE path delay. Since the cooperative routing approach of SCIR produces a more evenly balanced level of network traffic, the mean MLU is reduced, which in turn (from Equation 1) reduces the mean link delay.

- In Figures 6.11 and 6.17, higher throughput levels for SCIR are realised compared to SP and also for the optimised link weight setting in comparison to the original link weight setting. This is due to the higher capacity of SCIR on ETE paths, as the result of more balanced traf- fic across the network, and is achieved in spite of increased link utilisation.

- A general trend in the results of Sections 6.6.1 and 6.6.2 shows less SCIR improvement in optimised link weight setting in comparison to SCIR improvement in original link weight setting.This can be interpreted as the closer proximity of the results to those of global opti- mum solution for the optimised link weight setting compared to the original link weight

setting. This means that the margin for SCIR improvement compared to SP is less in the GA optimised solution, because the GA SP has already improved the initial SP results noticea- bly.

- It is generally believed that GA produces results close to optimal solutions. Therefore, the minimum value of MLU as well as the minimum ETED value might be within the close vi- cinity of near optimal traffic balancing results.

- The results of Section 6.6.3 that is focused on SP optimisation show that the improvement margins for SP results of all QoS metrics is more in SP-focused GA optimisation. At the same time, SCIR results of all QoS metrics achieve further enhancement in combined opti- misation of SP and SCIR. This matches the purpose of the fitness function in each case.

6.8

Summary

SCIR simultaneously satisfies the objectives of ISP and end-user, by providing path options that reduce the Maximum Link Utilisation and End-to-End path delay and increase the throughput, sim- ultaneously. However, the periodic recalculation of new paths to cope with the dynamics of online traffic makes SCIR relatively unscalable in larger networks.

To address this issue, and also improve the performance of SCIR in reducing MLU and ETE de- lay, a Genetic Algorithm (GA) was developed to optimise the results. Simulations on GEANT and Abilene demonstrated the capability of GA to optimise both SP and SCIR to further reduce the MLU and ETE delay and increase the throughput, across a wider range of QoS-Sensitive traffic compared to the original shortest path setting. In addition, the genetic algorithm can be generalised to produce various sets of desirable results based on the specified fitness function that takes into account the amount and range of improvement for MLU, ETED, and throughput.