  Vehicular Cloud Computing (VCC) is a new technological shift which exploits
the computation and storage resources on vehicles for computational service
provisioning. Spare on-board resources are pooled by a VCC operator, e.g. a
roadside unit, to complete task requests using the vehicle-as-a-resource
framework. In this paper, we investigate timely service provisioning for
deadline-constrained tasks in VCC systems by leveraging the task replication
technique (i.e., allowing one task to be executed by several server vehicles).
A learning-based algorithm, called DATE-V (Deadline-Aware Task rEplication for
Vehicular Cloud), is proposed to address the special issues in VCC systems
including uncertainty of vehicle movements, volatile vehicle members, and large
vehicle population. The proposed algorithm is developed based on a novel
Contextual-Combinatorial Multi-Armed Bandit (CC-MAB) learning framework. DATE-V
is `contextual' because it utilizes side information (context) of vehicles and
tasks to infer the completion probability of a task replication under random
vehicle movements. DATE-V is `combinatorial' because it aims to replicate the
received task and send the task replications to multiple server vehicles to
guarantee the service timeliness. We rigorously prove that our learning
algorithm achieves a sublinear regret bound compared to an oracle algorithm
that knows the exact completion probability of any task replications.
Simulations are carried out based on real-world vehicle movement traces and the
results show that DATE-V significantly outperforms benchmark solutions.
