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- Similarly, the classification of traffic (type-of-service identification) is of great importance in IoT networks. A Network Traffic Classifier (NTC) is an important part of current network management and administration systems. This classifier infers the service/application (e.g. HTTP, SIP...) being used by a network flow. This information is particularly important for Quality of Service (QoS) management, since the service used has a direct relationship with QoS requirements and user contracts/expectations. Network traffic identification is crucial for implementing effective management of network policy and resources in IoT networks, since the network may need to react differently depending on traffic profile information. The work in this thesis [2] provides a new technique for NTC that considers these problematics and the need to improve the accuracy of the classifiers. - Estimation of video QoE is important in current video transmission systems and its importance will grow with the new capabilities provides by the new network architectures (e.g. edge computing, cloud computing...), with more flexible network management systems which can make better use of quality estimates as perceived by the user of the services. The possibility of making a direct estimation of QoE from the network packets opens up the prospect of real-time quality of service (QoS) estimation, which is critical for the modern services infrastructure. In Fig 3. is presented a high-level view of the services available for the new IoT applications based on edge computing architectures [41]. Edge computing is a way to streamline the flow of traffic between cloud computing services and particular devices (e.g. IoT) and provide real- time local data analysis at the edge of the network, near the source of the data. This diagram shows the distribution of functions and services of modern networks architectures. The four areas, mentioned in previous sections, where machine learning can be applied to prediction and traffic analysis, can be allocated to the middle layer in Fig 3. This fact allows expanding the processing and distribution capabilities of new network services and is one of the main reasons for the expected future importance of machine learning in IoT networks. Fig 3. High level diagram of distribution and processing services for IoT applications. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 20PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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