The Emerging Internet Congestion Control Paradigms

This paper presents a survey on the emerging approaches to the end-to-end congestion control (EECC) in modern Internet. The actual mechanisms are inefficient in the operational contexts of complex and dynamic communication systems. The emerging learning-based approaches employ Machine Learning (ML) to either improve the existing schemes or to completely remodel congestion control from scratch. We classify ML algorithms according to the application area within the context of congestion control, and describe key classes of applications. We also discuss research and engineering issues regarding their implementation and their potential to be included within new operating systems.