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AuthorQiao, Wenxuan
AuthorDong, Ping
AuthorDu, Xiaojiang
AuthorZhang, Yuyang
AuthorZhang, Hongke
AuthorGuizani, Mohsen
Available date2022-10-11T09:04:47Z
Publication Date2022-05-01
Publication NameJournal of Parallel and Distributed Computing
Identifierhttp://dx.doi.org/10.1016/j.jpdc.2022.01.018
CitationQiao, W., Dong, P., Du, X., Zhang, Y., Zhang, H., & Guizani, M. (2022). QoS provision for vehicle big data by parallel transmission based on heterogeneous network characteristics prediction. Journal of Parallel and Distributed Computing, 163, 83-96.‏
ISSN07437315
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124297577&origin=inward
URIhttp://hdl.handle.net/10576/35006
AbstractMultipath 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.
SponsorThis work was supported by the Fundamental Research Funds for the Central University [grant 2020YJS021 ]; the National Natural Science Foundation of China (NSFC) [grant 61872029 ]; and the Beijing Municipal Natural Science Foundation [grant 4182048 ].
Languageen
PublisherAcademic Press Inc.
SubjectBig data
Multipath parallel transmission
Network bottleneck prediction
Quality of service
Vehicular networks
TitleQoS provision for vehicle big data by parallel transmission based on heterogeneous network characteristics prediction
TypeArticle
Pagination83-96
Volume Number163
dc.accessType Abstract Only


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