# Modeling and Profiling of Aggregated Industrial Network Traffic
## 4 Results and Discussion
### Discussion on Results and Related Works  
The overview of the proposed approach is illustrated in Figure 7. As presented through detailing the two-step approach in Section 3, it succeeds in identifying and profiling communication states in each work-cycle relevant to the operational mods. The two objectives of work-cycle recognition and state modeling serve the main goal of network traffic profiling. The defined network-level parameters, with respect to traffic dynamics, provided the basis for quantifying communication intensity. From the first objective, the continuous data stream was segmented into non-overlapping windows that approximated the boundaries of the work cycles. This process reduced the dimensions by excluding those with similar information, i.e., the repeated patterns. The result of the first objective was input for modeling the network traffic in each work-cycle. Since the data is sequentially observed, HMM was selected as an appropriate method for modeling the network dynamically. The order of the model was estimated using information gained from various methods, and a second-order HMM was used for the state identification step. The results showed dynamic similarities in the segments belonging to each state with both temporal and spatial features. The identified states then labeled the dataset. The summary statistics of each state, along with the defined parameter indicators, provided the basis for profiling network traffic dynamics based on the states of transmission intensity and throughput. The effectiveness of this approach was demonstrated through the validation scenarios and comparative analysis of the results with a set of learning algorithms. The experimental results supported the importance of work-cycle identification as a prerequisite for traffic modeling when aggregated traffic is under study. Furthermore, the modeling process utilizing methods that take the order of the observations into account shows better performance than the tree or graph-based methods. The industrial networks are under constant transformation either by integrating new technologies or adding advanced IT services to enhance flexibility, efficiency, and innovation. The former demands precise resource planning to avoid interruption of the ongoing operations, and the latter emphasizes data availability for additional insights into the system. In either case, imposed network changes require appropriate continuous adaptation, which is not a trivial task for network resource management. It calls for the full perception of the demands from the legacy systems and the new technology or the service, as well as a prediction of the consequences of their interactions in the ongoing network condition. In the absence of sufficient research works addressing the lack of insight into brown-field installations and securing the performance of ongoing processes upon integration of new technologies and applications, the presented work undertakes the orderly steps to address the aforementioned requirements. The proposed approach extends the network-level characterization to aggregated traffic modeling for network resource management. The influence of this approach can be associated with the related works and the state of practice from various aspects. The identified work-cycle boundaries reduce the model's state-space while increasing the sample space for the learning process. The reduced dimensions by temporal segmentation remove the unnecessary complexity of stream modeling imposed by data points with insignificant information gain. It can potentially be combined with the modeling methods proposed in [8] to derive alternative solutions for communication state recognition, and improve the modeling results presented in [13; 15] for more accurate  
Figure 7: Overview of the proposed approach with the obtained results when applied on aggregated traffic classes data. network traffic profiling.