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

• We abstract a multipath parallel heterogeneous network node mapping model. • We propose a fast convergent network characteristics prediction algorithm. • We design a packet granularity scheduling scheme for multipath communication. • We apply the new results to some known network simulation models. 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. [ABSTRACT FROM AUTHOR]