UC San Diego

This study aims at using Road-side Unit (RSU)-assisted Vehicular Edge Computing (VEC) systems to support nowadays compute-intensive and delay-sensitive vehicular applications. Vehicles can offload these applications to the VEC server at the RSU to ease the burden on their limited onboard computing resources. Although VEC servers usually have higher computing capacity than vehicles, their computing resource, as well as the RSU's communication and energy resources, are not unlimited due to deployment constraints and operating costs. Our goal is to find the optimal resource allocation strategies for the RSU-assisted VEC systems to enable low-latency compute-intensive vehicular applications for vehicles.

In the first part of the study, we consider Solar-powered RSU-assisted VEC systems, where the RSUs are solely powered by solar energy. Firstly, we aim to minimize service disruption of the offloaded vehicular applications under the intermittent solar power supply. We propose a two-phase approach that jointly optimizes solar energy usage and storage, user association, and RSU’s computing and communication resource allocation for the involved RSUs and vehicles in the VEC system. Secondly, we further reduce the application’s execution delay by a framework that uses computing resources from both the VEC server and the vehicle's local computing (VLC) unit for application execution through task partitioning and offloading. Furthermore, the framework is able to adjust the VEC server’s platform configuration and balance between the vehicular application’s execution delay and its application-level performance, according to the available computing, communication, and solar energy resources of the Solar-powered RSU.

In the second part of the study, we focus on using the RSU-assisted VEC system to support an emerging advanced vehicular application, the multi-vehicle perception fusion. It is challenging to effectively allocate RSU’s computing and communication resources to support the multi-vehicle perception fusion application due to its complex and uncertain task composition natures. To minimize the end-to-end delay of the above application, we present a real-time mechanism that jointly determines the optimal RSU's computing and communication resource allocation, as well as task partitioning and scheduling strategies, which are adaptive to the dynamic task composition of the application.