## 6 Conclusions and Future Work  
Traffic classification and prediction are essential for network resource management, QoS provisioning and monitoring, security, and network failure detection. For example, proactive-based network management, resource allocation, and traffic scheduling can be performed using the prediction result. An organized introduction of intelligence in the network is required to have automated network control and management. Using multi-agent-based service-orientation, a MANA-NMS architecture was presented in our previous work.  
In this work, we designed and implemented traffic classifier and predictor agents. Both NTCA and NTPA leverage ML/DL as an internal cognitive component of the agents to use in their decision process. The agents are autonomous in performing their respective decision. Using different cognition elements in the design and implementation, the performance of both agents is evaluated in terms of classification and prediction accuracy, classification and prediction latency, training latency, and computation resource utilization. The result suggests that LSTM-based NTPA and DT-based NTCA perform better than the rest in terms of prediction and classification accuracy and latency, respectively.  
As a future work, a realistic testbed implementation is required. Moreover, a complete system using the autonomous and atomic agents as a building block is necessary. However, we implemented only NTPA and NTCA in this work. Therefore, it is necessary to have other services, such as routing, QoS monitoring, 5G core access and mobility management function (AMF), session management function (SMF), firewall, deep packet inspection, and other novel functions to have a complete MANA-NMS model for network systems, such as 5G wireless network, autonomic network management, etc. Thus, it is an open challenge to implement such functions as intelligent agents. Furthermore, the agent is expected to be deployed in a container or virtual environment. The experimental evaluation of such deployment is also another open challenge that we will be working on.  
Figure 18: Comparison of NTCA CPU usage.  
**Author Contributions:** Conceptualization, methodology, formal analysis, investigation, supervision, original draft preparation, writing--review and editing, S.T.A.; methodology, formal analysis, writing--review and editing, original draft preparation, Z.A. and M.E.; resources, supervision, project administration, writing--review and editing, and funding acquisition, M.D.; writing--review and editing, supervision and funding acquisition, F.G.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been partially funded by NATO Science for Peace and Security (SPS) Program in the framework of the project SPS G5428 \"Dynamic Architecture based on UAVs Monitoring for Border Security and Safety\", and partially by the US National Science Foundation under the New Mexico SMART Grid Center-EPSCoR cooperative agreement Grant OIA-1757207.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.