Wanna Make Your TCP Scheme Great for Cellular Networks? Let Machines Do It for You!

Soheil Abbasloo, Chen Yu Yen, H. Jonathan Chao

Research output: Contribution to journalArticlepeer-review

Abstract

Can we instead of designing yet another new TCP algorithm, design a TCP plug-in that can enable machines to automatically boost the performance of the existing/future TCP designs in cellular networks? We answer this question by introducing DeepCC. DeepCC leverages advanced deep reinforcement learning (DRL) techniques to let machines automatically learn how to steer throughput-oriented TCP algorithms toward achieving applications' desired delays in a highly dynamic network such as the cellular network. We used DeepCC plug-in to boost the performance of various old and new TCP schemes including TCP Cubic, Google's BBR, TCP Westwood, and TCP Illinois in cellular networks. Through both extensive trace-based evaluations and real-world experiments, we show that not only DeepCC can significantly improve the performance of TCP schemes, but also after accompanied by DeepCC, these schemes can outperform state-of-the-art TCP protocols including new clean-slate machine learning-based designs and the ones designed solely for cellular networks.

Original languageEnglish (US)
Article number9252929
Pages (from-to)265-279
Number of pages15
JournalIEEE Journal on Selected Areas in Communications
Volume39
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • TCP
  • bufferbloat
  • cellular network
  • congestion control
  • deep reinforcement learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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