Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning. Группа авторов
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СКАЧАТЬ model provides us with an insight about the process underlying the data. Moreover, a learning model also performs compression by fitting a rule to the data. This enables us to use less memory to store the data and less computation to process. Another use of supervised learning is outlier detection. In this case once the rule is learned, we focus on the parts of the data that are not covered by the rule. In other words, we identify the instances that do not follow the rule and/or are exceptions to the rule. These are outliers and imply anomalies requiring further analysis.

Schematic illustration of the supervised learning model.

      2.2.2 Unsupervised Learning

Schematic illustration of the unsupervised learning model. Schematic illustration of the reinforcement learning model.

      2.2.3 Reinforcement Learning

      AI/ML techniques have a vital list of applications in many network and service management tasks, including (but are not limited to) traffic/service classification and prediction for performance management; intrusion, malware identification, and attribution for security management; root cause analysis and fault identification/prediction for fault management; and resource/job allocation/assignment for configuration management. As discussed in Chapter, the growth in connected devices as well as new communication technologies from 5G+ to SDN to NFV persuade network and service management research to explore new methodologies from the AI/ML field [17].

      Given the current advances in networks/services AI/ML has found its place in performance management tasks for its ability to learn from big data to predict different conditions, to aggregate patterns, to identify triggers for operations and management actions. For example, traffic prediction has seen multiple ML‐based applications from time series forecasting [18] to neural networks [19, 20] to hidden Markov models [21] to genetic algorithms [22]. Moreover, many other tasks in performance management have employed AI/ML techniques for traffic management in the cloud and mobile edge computing, network resource management and allocation, Quality of Service assurance, and congestion control. These leverage the capabilities of AI/ML techniques to learn from temporal and dynamic data [23–26]. Current examples of such developments include Deep Neural Networks [27], transfer learning [28], Deep Reinforcement Learning [15, 29], and Stream online learning [30].

      In fault management, prediction and diagnosis of faults attracted widespread use of AI/ML techniques from online learning for change point detection to Neural Networks СКАЧАТЬ