Joint Offloading and Resource Allocation Schemes based on Different Users’ Requirements in Vehicular Edge Computing and Networks

Abstract

Vehicular Edge Computing (VEC) is an emerging optimistic computing paradigm that aims to decrease back-haul network traffic and service latency by extending cloud service provisioning to network edge vehicles. As the Internet of Things (IoT) and telecommunication industry continue to progress, several innovative applications/fea tures including image-aided navigation, autonomous vehicles, and face recognition are starting to appear. Cooperative, linked, and autonomous vehicles present in the net work generate various types of resource-intensive and delay-sensitive tasks. These au tomotive applications demand a minimum processing delay along with a large amount of computational power. However, these power and resource-constrained vehicles might not be able to process these vehicular applications. VEC is considered an innovative paradigm for enhancing vehicles’ performance by executing various applications to the edge cloud. VEC benefits users by providing computational resources to their proximity thus decreasing the latency, but VEC has limited computational capacity and cannot process all tasks for a larger number of requests. Moreover, each task has individual requirements such as some tasks are delay-sensitive, some tasks require lower energy consumption, some tasks require real-time information, and some vehicles might have low battery levels. Also, in a highly dynamic topology, smaller task execution across a VEC server results in higher energy consumption and communication latency. It is required to optimally select the tasks for offloading to address these issues. This research proposes efficient task-centric and classification-based resource allocation and offloading strategies based on individual users’ requirements and task prioritization. In this thesis, we have discussed two schemes. The first scheme aims to lower the sys tem cost in terms of processing time while considering the tasks’ priority. The second scheme suggests an algorithm to lower the value of both processing delay and energy consumption while considering tasks’ priority, energy requirements, and battery level of vehicles. These proposed schemes offload the tasks to the VEC server according to the individual task requirements along with reducing system costs.