Building a Flexible Framework Towards Autonomous Networking Using Machine Learning Techniques

机器学习（ML）在语音识别和图像处理等各个领域取得了前所未有的进步。机器学习在众多学科中的作用越来越重要。机器学习在对服务质量、性能和安全性有极高要求的下一代自治网络中也起着关键作用。ML用于网络的例子包括ML在网络异常检测、网络流量预测、预防性维护等方面的应用。机器学习是数据驱动的，它比复杂的基于算法或模型的网络解决方案更有效。然而，机器学习也很重要，分布式网络系统极大地阻碍了机器学习的有效性，因为每个节点只有有限的局部信息，并且从其他节点学习是复杂的。软件定义网络（SDN）通过将网络设备上的主要控制功能转换为逻辑上的中央控制器，实现了重大的范式转变。网络功能虚拟化（Network function virtualization， NFV）被广泛应用于网络功能虚拟化，为网络运营商提供资源管理、性能提升、创新业务部署等方面的灵活性。此外，带内网络遥测（INT）仍然是一项发展中的技术，它可以通过提供对ML至关重要的高网络数据可见性来改善当前的网络监控。除了ML， SDN对INT也有重大影响，因为SDN可以有效地请求和收集整个网络的网络状态和数据，从而实现有效的网络分析并大大简化ML的复杂性。NFV可以通过在独立且功能强大的设备上运行机器学习功能来促进机器学习。然而，ML仍然存在挑战，例如ML选项众多，训练时间长，以及针对不断变化的环境进行重复训练。本研究的主要目标是构建一个集SDN/NFG、ML和INT于一体的灵活系统框架。这些技术的无缝融合在简化机器学习网络应用方面具有巨大的潜力。该框架将被设计为促进（由人工操作员或自动）选择最适合特定问题的适当ML技术，例如交通控制或安全。该框架的开发将基于对网络数据的特征和模式以及不同应用问题的特征的深入分析。此外，拟议的研究还通过考虑物联网（IoT）和信息中心网络（ICN）以及跨问题域的迁移学习，超越了当前的网络范式和机器学习技术。长期目标是调查拟议的综合框架对新兴范式的适应性。迁移学习的目标是减少机器学习所需的训练时间，并在网络变化或跨类似领域时支持知识重用。

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.