Persistence of Vehicular-Augmented Mobile Edges

Abstract

Mobile edge computing (MEC) allows for the execution of delay-sensitive and context-aware applications close to the end-users while alleviating backhaul utilization and computation at the core network. A recent trend is to extend the capabilities of the MEC using the available resources of end-user devices beyond the edge, such as vehicles. Because of the highly mobile nature of such devices, the beyond-the-edge computing resources available at the time the MEC offloads a task decays over time. In this paper, we propose and investigate the persistence of groups of computational nodes in a vehicular-augmented MEC scenario. The groups are formed according to the time of arrival of vehicles and their co-location within a cell. Through the analyses of two real-world vehicular scenarios in the cities of Rome and Rio de Janeiro. On the one hand, our results show that the persistence is highly heterogeneous, depending on the vehicular traffic density and the time of day. On the other hand, we are able to identify periods during which the persistence is short and periods that show stronger stability -it becomes then possible to schedule tasks to specific time slots depending on the requirements and the expected persistence.