  Vehicular edge computing (VEC) is a promising technology to support real-time
vehicular applications, where vehicles offload intensive computation tasks to
the nearby VEC server for processing. However, the traditional VEC that relies
on single communication technology cannot well meet the communication
requirement for task offloading, thus the heterogeneous VEC integrating the
advantages of dedicated short-range communications (DSRC), millimeter-wave
(mmWave) and cellular-based vehicle to infrastructure (C-V2I) is introduced to
enhance the communication capacity. The communication resource allocation and
computation resource allocation may significantly impact on the ultra-reliable
low-latency communication (URLLC) performance and the VEC system utility, in
this case, how to do the resource allocations is becoming necessary. In this
paper, we consider a heterogeneous VEC with multiple communication technologies
and various types of tasks, and propose an effective resource allocation policy
to minimize the system utility while satisfying the URLLC requirement. We first
formulate an optimization problem to minimize the system utility under the
URLLC constraint which modeled by the moment generating function (MGF)-based
stochastic network calculus (SNC), then we present a Lyapunov-guided deep
reinforcement learning (DRL) method to convert and solve the optimization
problem. Extensive simulation experiments illustrate that the proposed resource
allocation approach is effective.
