  There is a rapidly growing interest in the use of cloud computing for
automotive vehicles to facilitate computation and data intensive tasks.
Efficient utilization of on-demand cloud resources holds a significant
potential to improve future vehicle safety, comfort, and fuel economy. In the
meanwhile, issues like cyber security and resource allocation pose great
challenges. In this paper, we treat the resource allocation problem for
cloud-based automotive systems. Both private and public cloud paradigms are
considered where a private cloud provides an internal, company-owned internet
service dedicated to its own vehicles while a public cloud serves all
subscribed vehicles. This paper establishes comprehensive models of cloud
resource provisioning for both private and public cloud- based automotive
systems. Complications such as stochastic communication delays and task
deadlines are explicitly considered. In particular, a centralized resource
provisioning model is developed for private cloud and chance constrained
optimization is exploited to utilize the cloud resources for best Quality of
Services. On the other hand, a decentralized auction-based model is developed
for public cloud and reinforcement learning is employed to obtain an optimal
bidding policy for a "selfish" agent. Numerical examples are presented to
illustrate the effectiveness of the developed techniques.
