Smart Communication Networks with Machine Learning

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In evolving towards the fifth generation (5G) and beyond, the communication networks have been experiencing a fundamental change in the way they are designed and operated. The shift is mainly due to the ever-increasing service demands, new use cases, and diverse applications, which render the traditional one-size-fits-all approach ineffective and inefficient. Software defined networking (SDN) and network function virtualization (NFV) are two emerging technologies that can be leveraged for a smart, agile, and cost-effective networking solution. Due to the complicate operation of modern communication networks, machine intelligence potentially provides a new avenue to deal with data traffic dynamics and to maximize network performance. This DG program will investigate several fundamental issues to develop innovative engineering solutions for smart and agile communication networks, taking advantage of state-of-the-art machine intelligence tools. First, we will study how to determine the network topology for a virtual network. The topology establishment requires joint routing and service function placement, which is a multi-objective optimization problem and entails high complexity. We will develop a dynamic network topology solution based on traffic load prediction. Second, we will investigate how to efficiently allocate multi-dimensional (computing, transmission, and storage) resources for desired service isolation among multiple virtual networks sharing the common physical substrate; and develop a resource management framework for maximal resource multiplexing while ensuring service quality. Lastly, we will study networking protocol customization for reliable end-to-end data transport. The required protocol depends on network and service functions associated with each service request. As a result, the protocol should be customized and created on demand. We will develop a protocol customization and automation solution. HQP training is an essential component of this DG application. Five PhD students and three postdoctoral fellows will participate in the proposed research. The research program will provide the trainees with valuable knowledge and research experience in communication networks and machine learning, and enable them to emerge as expert additions to the Canadian high technology industry labor force. The research outcomes are expected to have a profound scientific, technological, and social impact, in providing in-depth understanding and new insights for developing innovative cost-effective intelligent algorithms and protocols for future communication networks, to support various new use cases and information services. The race towards developing networking technologies based on machine intelligence will undoubtedly be undertaken by research groups worldwide. Invention of successful new techniques on the subject will foster a vital competitive edge to Canadian telecommunications industry in the international market place.