Congestion-Adaptive and Deadline-Aware Scheduling for Connected Car Services over Mobile Networks

Abstract

Road traffic collisions are an extremely serious and often fatal issue. One promising approach to mitigate such collisions is the use of connected car services that share road traffic information obtained from vehicles and cameras over mobile networks. In connected car services, it is important for data chunks to arrive at a destination node within a certain deadline constraint. In this paper, we define a flow from a vehicle (or camera) to the same vehicle (or camera) via an MEC server, as a mission critical (MC) flow, and call a deadline of the MCflow the MCdeadline. Our research objective is to achieve a higher arrival ratio within the MCdeadline for the MC flow that passes through both the radio uplink and downlink. We previously developed a deadline-aware scheduler with consideration for quality fluctuation (DAS-QF) that considers chunk size and a certain deadline constraint in addition to radio quality and utilizes these to prioritize users such that the deadline constraints are met. However, this DAS-QF does not consider that the congestion levels of evolved NodeB (eNB) differ depending on the eNB location, or that the uplink congestion level differs from the downlink congestion level in the same eNB. Therefore, in the DAS-QF, some data chunks of a MC flow are discarded in the eNB when they exceed either the uplink or downlink deadline in congestion, even if they do not exceed the MC deadline. In this paper, to reduce the eNB packet drop probability due to exceeding either the uplink and downlink deadline, we propose a deadline coordination function (DCF) that adaptively sets each of the uplink and downlink deadlines for the MC flow according to the congestion level of each link. Simulation results show that the DAS-QF with DCF offers higher arrival ratios within the MC deadline compared to DAS-QF on its own