The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with inputawareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm. Index Terms-Vehicular edge computing, task offloading, online learning, multi-armed bandit.
In REF , Sun et al. have studied dynamic task offloading problem in vehicular edge computing systems, and proposed an adaptive learning-based task offloading algorithm to minimize the average offloading delay, which enables each task vehicle to learn the delay performance of service vehicles in a distributed manner, without frequent exchange of state information, modifying the existing multi-armed bandit algorithms to be input-aware and occurrence-aware.
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
IEEE Transactions on Vehicular Technology
IEEE Transactions on Vehicular Technology
Mathematics Computer Science