ABS: Adaptive Buffer Sizing via Augmented Programmability with Machine Learning

Jiaxin Tang, Sen Liu, Yang Xu, Zehua Guo, Junjie Zhang, Peixuan Gao, Yang Chen, Xin Wang, H. Jonathan Chao

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Programmable switches have been proposed in today's network to enable flexible reconfiguration of devices and reduce time-to-deployment. Buffer sizing, an important factor for network performance, however, has not received enough attention in programmable network. The state-of-the-art buffer sizing solutions usually employ either fixed buffer size or adjust the buffer size heuristically. Without programmability, they suffer from either massive packet drops or large queueing delay in dynamic environment. In this paper, we propose Adaptive Buffer Sizing (ABS), a low-cost and deploy-friendly framework compatible with programmable network. By decoupling the data plane and control plane, ABS-capable switches only need to react to the actions from controller, optimizing network performance in run-time under dynamic traffic. Meanwhile, actions can be programmed by particular Machine Learning (ML) models in the controller to meet different network requirements. In this paper, we address two specific ML models for different scenarios, a reinforcement learning model for relatively stable network with user specific quality requirements, and a supervised learning model for highly dynamic network condition. We implement the ABS framework by integrating the prevalent network simulator NS-2 with ML module. The experiment shows that ABS outperforms state-of-the-art buffer sizing solutions by up to 38.23x under various network environments.

Original languageEnglish (US)
Title of host publicationINFOCOM 2022 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665458221
StatePublished - 2022
Event41st IEEE Conference on Computer Communications, INFOCOM 2022 - Virtual, Online, United Kingdom
Duration: May 2 2022May 5 2022

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference41st IEEE Conference on Computer Communications, INFOCOM 2022
Country/TerritoryUnited Kingdom
CityVirtual, Online


  • NS-2
  • adaptive buffer sizing
  • quality of experience
  • reinforcement learning
  • standing queue
  • supervised learning

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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