Gremlin: Scheduling Interactions in Vehicular Computing

Kyungmin Lee, Jason Flinn, and Brian D. Noble

Abstract

Vehicular applications must not demand too much of a driver's attention. They often run in the background and initiate interactions with the driver to deliver important information. We argue that the vehicular computing system must schedule interactions by considering their priority, the attention they will demand, and how much attention the driver currently has to spare. Based on these considerations, it should either allow a given interaction or defer it.

We describe a prototype called Gremlin that leverages edge computing infrastructure to help schedule interactions initiated by vehicular applications. It continuously performs four tasks: (1) monitoring driving conditions to estimate the driver's available attention, (2) recording interactions for analysis, (3) generating a user-specific quantitative model of the attention required for each distinct interaction, and (4) scheduling new interactions based on the above data.

Gremlin performs the third task on edge computing infrastructure. Offload is attractive because the analysis is too computationally demanding to run on vehicular platforms. Since recording size for each interaction can be large, it is preferable to perform the offloaded computation at the edge of the network rather than in the cloud, and thereby conserve wide-area network bandwidth.

We evaluate Gremlin by comparing its decisions to those recommended by a vehicular UI expert. Gremlin's decisions agree with the expert's over 90% of the time, much more frequently than the coarse-grained scheduling policies used by current vehicle systems. Further, we find that offloading of analysis to edge platforms reduces use of wide-area networks by an average of 15MB per analyzed interaction.