Quantitative performance analysis plays a pivotal
role in theoretically investigating the performance of Vehicular
Edge Computing (VEC) systems. Although considerable research
efforts have been devoted to VEC performance analysis, all
of the existing analytical models were designed to derive the
average system performance, paying insufficient attention to the
worst-case performance analysis, which hinders the practical
deployment of VEC systems to support mission-critical vehicular
applications, such as collision avoidance. To bridge this gap, we
develop an original performance analytical model by virtue of
Stochastic Network Calculus (SNC) to investigate the worst-case
end-to-end performance of VEC systems. Specifically, to capture
the bursty feature of task generation, an innovative bivariate
Markov Chain is firstly established and rigorously analysed to
derive the stochastic task envelope. Then, an effective service
curve is created to investigate the severe resource competition
among vehicular applications. Driven by the stochastic task
envelope and effective service curve, a closed-form end-to-end
analytical model is derived to obtain the latency bound for
VEC systems. Extensive simulation experiments are conducted
to validate the accuracy of the proposed analytical model under
different system configurations. Furthermore, we exploit the
proposed analytical model as a cost-effective tool to investigate
the resource allocation strategies in VEC systems.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)