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TripRes: Traffic Flow Prediction Driven Resource Reservation for Multimedia IoV with Edge Computing

Authors:

Xiaokang ZhouAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 2

Article No.: 41, Pages 1 - 21

https://doi.org/10.1145/3401979

Published: 21 April 2021 Publication History

Abstract

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

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Index Terms

TripRes: Traffic Flow Prediction Driven Resource Reservation for Multimedia IoV with Edge Computing

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 2

May 2021

410 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3461621

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2021 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2021

Accepted: 01 May 2020

Revised: 01 March 2020

Received: 01 August 2019

Published in TOMM Volume 17, Issue 2

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National Natural Science Foundation of China
Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps

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Cited By

Dubey VKumari PPatel KSingh SShrivastava S(2024)Amalgamation of Optimization Algorithms With IoT ApplicationsSustainable Development in Industry and Society 5.010.4018/979-8-3693-7322-4.ch009(176-204)Online publication date: 30-Jun-2024
https://doi.org/10.4018/979-8-3693-7322-4.ch009
Wang SMa XLi X(2024)A Parameter Adaptive Method for Image SmoothingTsinghua Science and Technology10.26599/TST.2023.901006829:4(1138-1151)Online publication date: Aug-2024
https://doi.org/10.26599/TST.2023.9010068
Li GWu HWu JLi Z(2024)Efficient and secure privacy protection scheme and consensus mechanism in MEC enabled e-commerce consortium blockchainJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00652-613:1Online publication date: 9-May-2024
https://dl.acm.org/doi/10.1186/s13677-024-00652-6
Li DZhang HCheng JLiu B(2024)Improving efficiency of DNN-based relocalization module for autonomous driving with server-side computingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00592-113:1Online publication date: 25-Jan-2024
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