Paper Info
Title
TripRes: Traffic Flow Prediction Driven Resource Reservation for Multimedia IoV with Edge Computing
Abstract
AbstractThe 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.
Year
DOI
Venue
2021
10.1145/3401979
ACM Transactions on Multimedia Computing, Communications, and Applications
Keywords
DocType
Volume
Resource reservation, edge computing, multimedia IoV, traffic flow prediction, residual networks
Journal
17
Issue
ISSN
Citations 
2
1551-6857
4
PageRank 
References 
Authors
0.39
22
6
Name
Order
Citations
PageRank
Xu Xiaolong142464.23
Zijie Fang240.39
Lianyong Qi356057.12
Zhang Xuyun495269.49
Qiang He521723.35
Xiaokang Zhou622525.50