Selective Traffic Offloading on the Fly: A Machine Learning Approach

Zaiyang Tang, Peng Li, Song Guo, Xiaofei Liao, Hai Jin, Daqing Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


It has been well recognized that network transmission constitutes a large portion of smartphone energy consumption, mainly because of the tail energy caused by cellular network interface. Traffic offloading has been proposed to reduce energy by letting a smartphone offload network traffic to its neighbors in vicinity via low-power direct connections (e.g., WiFi Direct or Bluetooth). Our experiments conducted in a realistic environment reveal that energy efficiency cannot be improved or even deteriorates without a carefully designed offloading strategy. In this paper, we propose a selective traffic offloading scheme implemented as a smartphone middleware in a software-defined fashion, which consists of a packet classifier and a traffic scheduler. Using a light-weight machine learning approach exploiting unique smartphone context information, the packet classifieridentifies packets generated on the fly as offloadable or notwith substantially improved efficiency and feasibility on resource limited smartphones compared to traditional approaches. Both testbed and simulation based experiments are conducted and the results show that our proposal always attains the superior performance on a number of comparison metrics.

Original languageEnglish
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781538617915
Publication statusPublished - 13 Jul 2017
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - J.W. Marriott Hotel, Atlanta, United States
Duration: 5 Jun 20178 Jun 2017

Publication series

NameProceedings - International Conference on Distributed Computing Systems


Conference37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Country/TerritoryUnited States


  • Energy saving
  • Maching learning
  • Offloading

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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