# Intelligent Network Service Optimization in the Context of 5G/NFV
## 6 Conclusions  
In our research, we studied the MAPE framework for optimizing video streaming services, based on the innovative and emerging principles of IoT, CNC, 5G, and ML. One of the problems that NGN applications are facing is that, despite the fact that the technologies that we are using (i.e., SDN, NFV) support life-cycle management of runtime, it is difficult to guarantee the QoS using traditional monitoring mechanisms that identify the problems when they occur. In modern applications, in order to provide a specific experience level, we must analyse each service separately, predict the potential issues, and act proactively to avoid any negative impact on the QoS. In this context, in the presented work, we introduced the MAPE approach using the NFV architecture, and we presented preliminary results from prediction algorithms for transmitting visual content, given the requirements of the responding teams operating in the field, as expressed by the QoE MOS specification. According to the results, ML integration ensures the quality and trustworthiness of the optimization actions. From this perspective, it is established that the framework allows the system to react quickly in accordance with scaling factors and the placement of all available sets of resources, with the goal of optimizing the service's performance and consequently satisfying the users' requirements at execution time, especially under ambiguous networking and computational circumstances, by invoking parameters of a mutable nature such as the frame rate, bit rate, and packet loss. Furthermore, the deployment of inherent security features renders our developed MAPE architecture fundamentally secure, ensuring the robustness of the system, as well as the integrity and privacy of stored and transported information.  
Nevertheless, this research should be furthered and the next milestones ought to be set. To the best of our knowledge, there are currently no available datasets from other MAPE implementations to contrast with our solution. The provision of a justified comparison to other relevant solutions, as soon as they become available, is a priority in our research roadmap.  
Moreover, the effort to extend the set of ML algorithms, with the target of offering better coverage of the users requirements, while also training new models better and  
Figure 9: NS state class and scaling status. (**a**) NS state classification; (**b**) NS scaling status.  
offering them as open-source models, represents a milestone in our forthcoming work. Alternatively, possible optimization solutions utilizing VNE optimization strategies could be substituted for the established OE, in order to extend the comparison baseline for optimization models.  
Additional steps can be taken regarding the enrichment of MAPE's by-design security. The integration of blockchain practices may prove to be a key security measure, to completely avoid the possibility of corrupted QoE MOS values and misleading states in action mapping. Blockchain adopts the same decentralized and distributed directives as the MAPE framework, rendering it compatible with the principles of MAPE's core architecture. However, there are certain limitations that should be challenged. Blockchain ledgers can grow very large over time, which may lead to storage depletion. Furthermore, a substantial sacrifice in response time and slower optimization actions are possible eventualities.  
Conceptualization, P.A.K. and P.T.; methodology, P.A.K. and K.R.; software, K.R.; formal analysis, investigation K.R. and P.A.K.; data curation, K.R.; writing-original draft preparation, S.P., P.A.K. and K.R.; writing-review and editing, P.A.K., H.C.L. and P.T. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by FASTER and has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 833507.  
Not applicable.  
The authors declare no conflict of interest.