QoS provision for vehicle big data by parallel transmission based on heterogeneous network characteristics prediction

Wenxuan Qiao, Ping Dong, Xiaojiang Du, Yuyang Zhang, Hongke Zhang, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Multipath parallel transmission has become an important research direction to improve big data transmission efficiency of connected vehicles. However, due to the heterogeneity and time-varying characteristics of parallel transmission paths, packets transmitted in parallel are usually out-of-order delivered to the destination, which greatly limits the throughput. To Lift the restriction of out-of-order delivery on the efficiency of big data transmission, this paper proposes a packet-granular real-time shortest delay scheduling scheme for multipath transmission based on path characteristics prediction. The scheme first clusters and models the heterogeneous network, which greatly reduces the complexity of the network. Subsequently, a prediction algorithm that can quickly converge to real-time delay is proposed. Then the details of the scheduling scheme are introduced by modules, and the bandwidth aggregation efficiency close to the theoretical upper limit is proved through simulation. Finally, we summarize the applicable scenarios and future work of the scheme.

Original languageEnglish
Pages (from-to)83-96
Number of pages14
JournalJournal of Parallel and Distributed Computing
Volume163
DOIs
StatePublished - May 2022

Keywords

  • Big data
  • Multipath parallel transmission
  • Network bottleneck prediction
  • Quality of service
  • Vehicular networks

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