Network for AI and AI for Network: Challenges and Opportunities for Learning-Oriented Networks
Jianping Pan, Lin Cai, Shen Yan, Xuemin Shen
Abstract
The “data pipe” model used by the existing Internet protocol stack is no longer ideal for many emerging applications, due to multimedia, multicast, mobility, machine learning, and network management challenges. A new learning-oriented network architecture is required to deal with these challenges and serve learning-centric applications in data centers, around network edges, and on mobile devices. This article focuses on the network for AI and AI for network for learning-oriented network architecture. This is done by leveraging, improving, and creating new learning techniques to determine and optimize protocol mechanisms and control policies. The new network architecture can provide ample research opportunities in network topology control, protocol design, and performance evaluation, aiming to network a truly dependable cyber-infrastructure. The learning-oriented network can also learn from applications and communications automatically and continuously while running on different infrastructures to support diverse requirements. In addition, the network can keep evolving its protocol mechanisms and control policies in an online manner. It does this while maintaining protocol security and preserving user privacy, to learn and perform more effectively and efficiently. Finally, the main challenges and opportunities of learning-oriented network are discussed, encouraging further research.