# Cloud Resource Allocation for Cloud-Based Automotive Applications

## TL;DR

This paper explores resource allocation strategies for cloud-based automotive systems, proposing models for private and public clouds that optimize resource use considering delays and deadlines, using advanced optimization and learning techniques.

## Contribution

It introduces comprehensive resource provisioning models for both private and public cloud automotive systems, employing chance constrained optimization and reinforcement learning.

## Key findings

- Effective resource allocation models improve Quality of Service.
- Decentralized auction-based approach adapts to 'selfish' vehicle agents.
- Numerical results demonstrate the effectiveness of proposed techniques.

## Abstract

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.