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Gremlin: scheduling interactions in vehicular computing

Published: 12 October 2017 Publication History

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.

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cover image ACM Conferences
SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing
October 2017
365 pages
ISBN:9781450350877
DOI:10.1145/3132211
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Publication History

Published: 12 October 2017

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Author Tags

  1. driver distraction
  2. edge offload
  3. vehicular applications

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SEC '17
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SEC '17: IEEE/ACM Symposium on Edge Computing
October 12 - 14, 2017
California, San Jose

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SEC '17 Paper Acceptance Rate 20 of 41 submissions, 49%;
Overall Acceptance Rate 40 of 100 submissions, 40%

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  • (2022)EdgeWare: toward extensible and flexible middleware for connected vehicle servicesCCF Transactions on High Performance Computing10.1007/s42514-022-00100-44:3(339-356)Online publication date: 9-May-2022
  • (2021)CLONE: Collaborative Learning on the EdgesIEEE Internet of Things Journal10.1109/JIOT.2020.30302788:13(10222-10236)Online publication date: 1-Jul-2021
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  • (2020)EdgeFed: Optimized Federated Learning Based on Edge ComputingIEEE Access10.1109/ACCESS.2020.30382878(209191-209198)Online publication date: 2020
  • (2020)Online Offloading Scheduling and Resource Allocation Algorithms for Vehicular Edge Computing SystemIEEE Access10.1109/ACCESS.2020.29810458(52428-52442)Online publication date: 2020
  • (2019)Computation Offloading Toward Edge ComputingProceedings of the IEEE10.1109/JPROC.2019.2922285107:8(1584-1607)Online publication date: Aug-2019
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