DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-19
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Telecommunications Engineering
Abstract EN
In recent years, the volume of global video traffic has been increasing rapidly and it is considerably significant to offload the traffic during the process of video transmission and improve the experience of users.
In this paper, we propose a novel traffic offloading strategy to provide a feasible and efficient reference for the following 2022 FIFA World Cup held in Qatar.
At first, we present the system framework based on the Mobile Edge Computing (MEC) paradigm, which supports transferring the FIFA World Cup traffic to the mobile edge servers.
Then, the Deep Reinforcement Learning (DRL) is used to provide the traffic scheduling method and minimize the scheduling time of application programs.
Meanwhile, the task scheduling operation is regarded as the process of Markov decision, and the proximal policy optimization method is used to train the Deep Neural Network in the DRL.
For the proposed traffic offloading strategy, we do the simulation based on two real datasets, and the experimental results show that it has smaller scheduling time, higher bandwidth utilization, and better experience of user than two baselines.
American Psychological Association (APA)
Li, Hongyi& Che, Xinrui. 2020. DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1192505
Modern Language Association (MLA)
Li, Hongyi& Che, Xinrui. DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic. Mobile Information Systems No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1192505
American Medical Association (AMA)
Li, Hongyi& Che, Xinrui. DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1192505
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192505