# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 8 Final Discussion and Conclusions  
In this paper, we have reviewed the main achievements obtained in the ALLIANCE project, a 45-month Spanish-funded national project started in 2018 and currently approaching its end. We have presented the novel architecture composed by 5 key blocks: the monitoring element, the trust element, the SDN/NFV cross-layer domain, the RINA domain, and the KDN orchestration layer. This project has focused on providing several \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **Traffic Type** & **Min. Latency** & **Max. Latency** & **Avg. Latency** & **Avg. Packet** \\  & & **(ms)** & **(ms)** & **(ms)** & **Loss** \\ \hline \multirow{6}{*}{full RINA} & HD video call & 0.190095 & 1.0508 & 0.51765 & 0 \\ \cline{2-6}  & Online gaming & 0.195035 & 1.0397 & 0.51995 & 0 \\ \cline{2-6}  & VoIP & 0.184455 & 1.0033 & 0.51845 & 0 \\ \cline{2-6}  & File sharing & 0.18789 & 217.07 & 15.419 & 0.000643 \\ \cline{2-6}  & Interactive & 9.196455 & 198.04 & 14.492 & 1.418555 \\ \hline \multirow{6}{*}{TCP/IP-like} & HD video call & 0.16851 & 245.21 & 18.3975 & 0.073067 \\ \cline{2-6}  & Online gaming & 0.17064 & 239.18 & 18.5635 & 0.102037 \\ \cline{1-1} \cline{2-6}  & VoIP & 0.155495 & 245.145 & 18.112 & 0.084521 \\ \cline{1-1} \cline{2-6}  & File sharing & 0.144350 & 240.945 & 18.232 & 0.101328 \\ \cline{1-1} \cline{2-6}  & Interactive & 0.15236 & 245.89 & 20.9095 & 0.094567 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Latency and packet loss at 90% offered load. solutions to different networking problems in this 5G/B5G era. Results obtained in each of these blocks show superior performance with respect to the state of the art and motivate us to continue with these solutions in our future activities. For instance, we have investigated new modulation formats and new design tools for both the access and the transport segments of a high-capacity and ultra-low-latency optical infrastructure. We have proposed an enhanced fixed mobile convergence using a NOMA-CAP wireless waveform with a single-carrier wired signal in a PON scenario. Although in its infancy, RINA has showed the potentiality to be a viable solution for B5G. Security, programmability, virtualisation and mobility are inherent part of the architecture by design, largely simplifying the control and management tasks between different network segments and operators. Indeed, we have showed in this work how it is possible in RINA to control and guarantee the application-specific QoS requirements between different providers. Although recent advances in AI have led to a new era of ML techniques and the application of ML to networking is today a consolidated focus on the research community, it has not fulfilled its high expectations yet. In fact, existing state-of-the-art proposals seem unable to meet and outperform traditional approaches. In ALLIANCE, we have presented our KDN framework, which provides a suitable and practical environment for large-scale communications, as the network can learn from data by itself and provides efficient and automatic answers to most of the possible events, in a simpler, smarter, safer, and speedier way. For example, we have pioneered the first application of GNN to computer networks enabling the prediction of precise per-flow performance metrics for known networks and producing accurate estimates for unseen networks, outperforming state-of-the-art schemes in terms of accuracy and cost. Another example is the MAPE approach, which can autonomously maintain the desired QoS levels of the deployed services over the time, even under adverse dynamic conditions. These noteworthy contributions pave the way for an intelligent network knowledge plane which, making use of accurate and trusted data, can provision end-to-end services, with QoS guarantees, across one (or several) underlying Network Service Providers (NSPs). Our future works will continue in this direction aiming at investigating new ways to accelerate GNN inference via software and hardware techniques.