research-article

DeepSave: saving DNN inference during handovers on the edge

Authors:

Bing Bing ZhouAuthors Info & Claims

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

Pages 166 - 178

https://doi.org/10.1145/3318216.3363301

Published: 07 November 2019 Publication History

Abstract

Recent advances in deep neural networks (DNNs) have substantially improved the accuracy and speed of a variety of intelligent applications, for example, real-time video classification. However, one of the challenges is how to maintain the quality of service during handovers to avoid interruptions. Inspired by the recently developed DNN partition schemes, where the DNN model inference can be partitioned and jointly processed at a mobile device and its connected edge-computing server, we propose DeepSave, a promising solution to save a large portion of consecutive video frames that cannot be handled during handovers¹. DeepSave comprises two subschemes: (1) The Frame Choosing Scheme is to determine which frames we should save during a handover, to maximize the number of saved frames while preserving the accuracy of the inferences. (2) The Last Arriving Frame Repartition Scheme, with a provable performance bound, is to handle the last frame before the end of the handover as soon as possible, so that the arriving frames after the handover can be processed as usual without causing congestion. We have built up a real-world prototype and conducted field experiments and extensive simulations, showing that DeepSave can save up to 50.98% frames during handovers, which is much more than the benchmark schemes.

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Zheng YCui LTso FLi ZJia W(2024)DNN acceleration in vehicle edge computing with mobility-awarenessComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110607251:COnline publication date: 1-Sep-2024
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Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: Dec-2024
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Index Terms

DeepSave: saving DNN inference during handovers on the edge
1. Networks
  1. Network performance evaluation
    1. Network performance modeling

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Published In

cover image ACM Conferences

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

November 2019

455 pages

ISBN:9781450367332

DOI:10.1145/3318216

General Chairs:
Songqing Chen
George Mason University
,
Ryokichi Onishi
Toyota
,
Program Chairs:
Ganesh Ananthanarayanan
Microsoft Research
,
Qun Li
College of William & Mary

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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New York, NY, United States

Publication History

Published: 07 November 2019

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SEC '19

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SIGMOBILE

SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing

November 7 - 9, 2019

Virginia, Arlington

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SEC '19 Paper Acceptance Rate 20 of 59 submissions, 34%;

Overall Acceptance Rate 40 of 100 submissions, 40%

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Cited By

Wu JWang LJin QLiu F(2024)Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-AlignmentIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.334051835:2(280-296)Online publication date: Feb-2024
https://doi.org/10.1109/TPDS.2023.3340518
Zheng YCui LTso FLi ZJia W(2024)DNN acceleration in vehicle edge computing with mobility-awarenessComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110607251:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110607
Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: Dec-2024
https://doi.org/10.1109/COMST.2023.3323091
Xue MWu HPeng GWolter K(2022)DDPQN: An Efficient DNN Offloading Strategy in Local-Edge-Cloud Collaborative EnvironmentsIEEE Transactions on Services Computing10.1109/TSC.2021.311659715:2(640-655)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TSC.2021.3116597
Xie APeng Y(2022)Improving the Quality of Inference for Applications using Chained DNN Models during Edge Server Handover2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)10.1109/SEC54971.2022.00079(516-520)Online publication date: Dec-2022
https://doi.org/10.1109/SEC54971.2022.00079
Ju WYuan DBao WGe LZhou B(2021)eDeepSave: Saving DNN Inference using Early Exit During Handovers in Mobile Edge EnvironmentACM Transactions on Sensor Networks10.1145/344726717:3(1-28)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3447267
Dong CHu SChen XWen W(2021)Joint Optimization With DNN Partitioning and Resource Allocation in Mobile Edge ComputingIEEE Transactions on Network and Service Management10.1109/TNSM.2021.311666518:4(3973-3986)Online publication date: Dec-2021
https://doi.org/10.1109/TNSM.2021.3116665

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