5G and edge computing will serve various emerging use cases that have diverse requirements for multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology for creating virtual networks that can be customized according to the requirements of different use cases. Provisioning network slices requires end-to-end resource orchestration which is challenging. This talk will discuss the challenges of end-to-end network slicing in wireless edge computing systems and present machine learning assisted network slicing solutions. First, the design of a new decentralized cross-domain resource orchestration solution will be presented. This solution optimizes the cross-domain resource orchestration while providing the performance and functional isolations among network slices. Second, a decentralized deep reinforcement learning algorithm will be designed to dynamically orchestrate resources for end-to-end network slicing. The system implementation and testbed design of the end-to-end network slicing system will also be discussed. Finally, future research directions in designing end-to-end network slicing solutions with machine learning will be shared.

Co-sponsored by: North Jersey Section, Signal Processing Chapter,

Speaker(s): Dr. Tao Han,

Agenda:
5G and edge computing will serve various emerging use cases that have diverse requirements for multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology for creating virtual networks that can be customized according to the requirements of different use cases. Provisioning network slices requires end-to-end resource orchestration which is challenging. This talk will discuss the challenges of end-to-end network slicing in wireless edge computing systems and present machine learning assisted network slicing solutions. First, the design of a new decentralized cross-domain resource orchestration solution will be presented. This solution optimizes the cross-domain resource orchestration while providing the performance and functional isolations among network slices. Second, a decentralized deep reinforcement learning algorithm will be designed to dynamically orchestrate resources for end-to-end network slicing. The system implementation and testbed design of the end-to-end network slicing system will also be discussed. Finally, future research directions in designing end-to-end network slicing solutions with machine learning will be shared.

Room: M105, Bldg: Muscarelle Center, M105, , 1000 River Road , Teaneck , New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/301143