## 6 Future Work and Open Challenges  
A key result of this paper is that the mean RTT of NMG control data exceeds the industrial communication response time requirements by a factor of 25 in the presence of VS. Maintaining the RTTs of NMG control data at tolerable levels is crucial to prevent system-wide stability and performance issues [70] for operational functions. As future work, machine learning (ML) prediction algorithms will be used to forecast future RTTs to ensure that QoD remains within industrial latency requirements. Various techniques exist for predicting RTTs in communication networks [71]. Recent papers explore ML algorithms for the future states of QoD metrics. In [72], an ML ensemble prediction model is employed for RTT prediction in 4G and 5G networks. The experiments demonstrate that the developed ensemble prediction model effectively predicts future RTTs. The authors of [73] implement a recurrent neural network (RNN) model that forecasts future RTTs in a communication network. They demonstrate that the implemented model outperforms traditional RTT prediction techniques. In [74], various ML models are employed for RTT prediction in cellular networks. The authors conduct two experimental scenarios where a smartphone is connected to the real cellular network in a suburban area in Italy. The results show that the random forest (RF) and decision tree (DT) prediction models achieve precision scores of 0.84 and 0.92 for the two experimental scenarios, respectively. Several ML techniques have been implemented to predict other QoD metrics in congested communication networks. A packet loss predictor, using two XGboost ML models, is developed for the TCP Reno and the cubic congestion control algorithms in [75]. The mininet network emulator is used to represent experimental scenarios where the background traffic is present. The results demonstrate successful packet loss prediction, and explicit congestion notification (ECN) is used to regulate sender packet rates before packet loss occurs. As only two congestion control algorithms are considered; the performance of models remains untested with other control algorithms. The authors of [76] address the limitation of autoregressive filters in jitter prediction for QoD measurement of VS. To improve QoD measurement in VS services, they introduce a novel jitter estimator that improves ML classification algorithms' learning capabilities across different network congestion levels. By using off-the-shelf ML, RF, and DT, their approach achieves classification accuracy rates of 95.1% for high, 45.2% for medium, and 95.1% for low congestion levels. In [77], an RNN-based model is developed to predict jitter in a congested communication network. The novel jitter prediction algorithm, called ASAP jitter, is implemented to predict gradient changes in a jitter measurement of VS. These predictions are classified by a convolutional neural network (CNN). The jitter classification algorithm, FEAT jitter, is implemented to convert jitter time series measurements into the high-dimensional feature space. This conversion aims to enhance the accuracy of predicting and classifying future jitter measurement for VS. The results show that an accuracy of 84.5% is obtained for ASAP jitter and 91.6% for FEAT jitter in total. The challenges associated with ML prediction algorithms need to be addressed. NMGs are characterised by complex components and numerous variables. This complexity presents a challenge for ML prediction algorithms in accurately learning and representing the relationships among these variables [78]. Moreover, ML prediction algorithms require adequate computational resources, including large memory capacity and processing power, when trained on large-scale datasets [79]. These pose a challenge for the scalability of ML prediction algorithms in the context of NMGs. We assume that greater RTTs will occur when NMGs are deployed in a WAN, where the variance of background traffic is high. One of the open challenges is the need to examine RTTs of NMG control data in a WAN.