1 整体框架

在这里插入图片描述

2 拥塞控制

ML+TCP congestion control ->improve the network performance, for example:

classify congestive and non-congestive loss

Jayaraj, A., Venkatesh, T., Murthy, C.S.R.: Loss classification in optical burst switching networks using machine learning techniques: improving the performance of TCP. IEEE J. Sel. Areas
Commun. 26(6), 45–54 (2008)

forecast TCP throughput

Mirza, M., Sommers, J., Barford, P., Zhu, X.: A machine learning approach to tcp throughput prediction. In: ACM SIGMETRICS Performance Evaluation Review, vol. 35, pp. 97–108. ACM (2007)

for better RTT estimation

Nunes, B.A., Veenstra, K., Ballenthin, W., Lukin, S., Obraczka, : K.: A machine learning approach to end-to-end rtt estimation and its application to tcp, pp. 1–6. IEEE (2011)

formalizes the multi-user congestion control problem as an MDP and learns the optimum policy offline

Winstein, K., Balakrishnan, H.: Tcp ex machina: computer-generated congestion control. In: ACM SIGCOMM Computer Communication Review, vol. 43, pp. 123–134. ACM (2013)

adjusts its sending rate based on continuous profiling

Dong, M., Li, Q., Zarchy, D., Godfrey, P.B., Schapira, M.: Pcc: rearchitecting congestion control for consistent high performance. NSDI 1, 2 (2015)

examined the learnability of TCP CC, where RemyCC was used to understand what kinds of imperfect knowledge on network model would hurt the learnability of TCP CC more than others.

Sivaraman, A., Winstein, K., Thaker, P., Balakrishnan, H.: An experimental study of the learnability of congestion control. In: ACM SIGCOMM Computer Communication Review, vol. 44, pp. 479–490. ACM (2014)

using Q-learning to design the TCP CC
csdn介绍链接

Li, W., Zhou, F., Meleis, W., Chowdhury, K.: Learning-based and data-driven tcp design for memory-constrained iot. In: Distributed Computing in Sensor Systems, pp. 199–205. IEEE (2016)

3 好句

机器学习与计算机网络相互促进：
The advent of ML revolutions brings fresh vitality to computer network research whereas the improvement of network performance also provides better support for ML computations.
两个领域的结合：
The combination of computer network and ML technology is a frontier area and many open issues still remain to be explored.
It is believed that the combination of network and ML will generate more innovations and create more values in the near future.