Building a Flexible Framework Towards Autonomous Networking Using Machine Learning Techniques

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Machine learning (ML) has made unprecedented advancement in various areas, e.g., voice recognition and image processing. The role of ML is becoming more and more important in numerous disciplines. ML also plays a key role in next generation autonomous networking which has extreme high requirements in quality of service, performance, and security. Examples of ML for networking include applications of ML for network anomaly detection, network traffic prediction, preventive maintenance, etc. ML is data driven, which is more effective than complex algorithm- or model-based approaches to network solutions. However, ML is also nontrivial, and the distributed network systems tremendously hinder the effectiveness of ML, as each node only has limited local information and learning from other nodes is complex. Software-defined networking (SDN) has made a major paradigm shift by converting main control functions on network devices into a logically central controller. Network function virtualization (NFV) has often been adopted by virtualizing network functions that provide flexibility for network operators in managing resources, improving performance, and deploying innovative services. Further, in-band network telemetry (INT), still a developing technique, can improve current network monitoring by providing high network data visibility which is crucial for ML. In addition to ML, SDN also has a substantial impact on INT, as SDN can efficiently request and gather network states and data from INT about the entire network, which enables effective network analytics and greatly simplifies the complexity of ML. On the other hand, NFV can facilitate ML by running ML functions on a separate and powerful device. However, there are still challenges for ML, such as many ML options, long training time, and repetitive training for changing environments. The main objective of the proposed research is to build a flexible system framework that integrates SDN/NFG, ML, and INT. The seamless fusion of those technologies has substantial potential in simplifying the adoption of ML for networking. The framework will be designed to facilitate the selection (either by a human operator or automatically) of an appropriate ML technique that is best suitable for a specific problem, e.g., traffic control or security. The development of the framework will be based on thorough analyses of the characteristics and patterns of network data and the features of different application problems. Moreover, the proposed research is also looking beyond the current network paradigm and ML technologies by considering Internet of Things (IoT) and Information-centric Networking (ICN), and transfer learning across problem domains. The longer-term goal is to investigate the adaptation of the proposed integrative framework for the emerging paradigms. Transfer learning is targeted to reduce the training time needed for ML and to support knowledge reuse when network changes or across analogous domains.