Adaptive Energy-Minimized Scheduling of Real-Time Applications in Vehicular Edge Computing

Vehicular edge computing is a promising new computing paradigm that has lower service latency and higher bandwidth than cloud computing. However, the geographical dispersion of edge computing resources and the high dynamics of vehicles pose many challenges to its service provision. Aiming to minimize the energy consumption of vehicular edge computing servers, this article presents an adaptive scheduling approach for handling dynamic real-time computing requests. An auction-bid scheme is developed for deciding the roadside unit (RSU) to respond to the computing request, where the computing request is auctioned and the RSU with the least energy consumption gets the bid. This scheme works in a decentralized model that effectively reduces its implementation complexity. To process the computing request modeled as a directed acyclic graph (DAG) application, the upward rank value is used to decompose a DAG into individual tasks, and a deadline-aware queue jump algorithm is proposed to assign them to servers' queues in a specific RSU. A group scheduling scheme is developed to assign several applications as a group, for the purpose of searching for a better schedule. Extensive experiments are carried out to compare our proposed approach to some other heuristic and state-of-the-art approaches, and the results confirm the benefits of our proposed approach in terms of minimizing system energy consumption and providing a quick response to the computing request.