A Machine Learning Approach to End-to-End RTT Estimation and its Application to TCP

@article{Nunes2011AML,
  title={A Machine Learning Approach to End-to-End RTT Estimation and its Application to TCP},
  author={Bruno Astuto A. Nunes and Kerry Veenstra and William Ballenthin and Stephanie M. Lukin and Katia Obraczka},
  journal={2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)},
  year={2011},
  pages={1-6}
}
In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the Experts Framework. In our proposal, each of several ``experts'' guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT. Through extensive simulations we show that the proposed machine-learning algorithm adapts very quickly… CONTINUE READING