What If VEC Is Moving: Probabilistic Model of Task Execution Through Offloading in Vehicular Computing Environments

Various computing approaches within vehicular networks, such as vehicular edge computing (VEC) and cloud computing, have been suggested to facilitate task offloading, aiming to improve user satisfaction. The features of vehicular networks, including the rapid movement of vehicles and the fluctuating distribution of vehicle densities, present challenges to task offloading with in the VEC. Numerous algorithms have been suggested to address these challenges and provide an effective task-offloading framework. This paper introduces a probabilistic model that analyzes task offloading across different computing tiers, alongside proposing a mobile computing paradigm tailored to the dynamic nature of vehicular networks (VN). This paradigm aims to maintain persistent connectivity and enhanced connection reliability despite mobility facilitating sustainable end-to-end service delivery. Building upon this premise, we propose a three-tier computing paradigm comprising Vehicle Edge of Things (VEoTC), VEC, and Cloud Computing (CC). Within the VEoTC tier, Service Vehicles (SV) equipped with computational resources serve as the mobile computing layer. The proposed model ensures continuous connectivity by extending the dwell time between the service requester and the vehicular computational resource. The model ensures that the relative speed between the service vehicle (representing computational resources) and the service requester remains constant while within the communication range. We proposed a probabilistic model for the end-to-end serving time of the proposed computing paradigm. Then, we computed the dwell time between the SV and the served vehicle based on real data published by Didi Chuxing GAIA Initiative for Chengdu city, China. Utilizing a simulated model, we illustrated the additional penalty incurred by the road side unit (RSU) handovers.