Computation Task Assignment in Vehicular Fog Computing: A Learning Approach via Neighbor Advice

Abstract

In the recent years, fog computing has been proposed as a promising paradigm to enhance the performance of latency-critical applications by hosting them within idle computation nodes (Fog Nodes, FNs) at the edge of the network. Such a paradigm has been further extended to the vehicular fog computing (VFC) scenario by leveraging available computing resources offered by modern vehicles, thus allowing them to process different types of computation tasks on behalf of onboard users or nearby vehicles. Within this context, the proper assignment of computation tasks to Vehicular FNs is an important issue that is currently under active research. To address this issue, online learning approaches, where the performances of the Vehicular FNs in terms of task execution delay are learnt via trial and error, are starting to gain in popularity. This is mainly motivated by the inherent uncertainty caused by mobility and fluctuating resource availabilities in VFC environments. However, since the process of learning from scratch in such a dynamic vehicular environment may lead to a degradation in the learning performance, this paper proposes the use of an advising mechanism, where a roadside unit (RSU) who has already learnt the performances of the vehicles within its range, uses its acquired knowledge to provide advice to a neighbor RSU who does not have enough experience allowing it to make efficient assignment decisions on its own. To evaluate this approach, we used realistic vehicular mobility traces to simulate the VFC scenario. The obtained results show that our proposed approach improves the learning performance compared to the case where no advice is leveraged.