IEACC: An Intelligent Edge-Aided Congestion Control Scheme for Named Data Networking With Deep Reinforcement Learning

As a promising implementation of Information-Centric Networking (ICN), Named Data Networking (NDN) has potential advantages over the TCP/IP network in content distribution, mobility support, etc. However, the research on NDN is still in its infancy, and congestion control, NDN's most important...

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Published in:IEEE eTransactions on network and service management Vol. 19; no. 4; pp. 4932 - 4947
Main Authors: Yang, Jiayu, Chen, Yuxin, Xue, Kaiping, Han, Jiangping, Li, Jian, Wei, David S. L., Sun, Qibin, Lu, Jun
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bandwidth
Clustering
Congestion
congestion control
Consumers
Data transmission
Deep learning
Detectors
fairness
Image edge detection
Internet
Machine learning
Named data networking
Packet loss
Reinforcement learning
Routers
TCP/IP (protocol)
TCPIP
Transmission rate (communications)
ISSN:1932-4537, 1932-4537
Online Access:Get full text
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Summary:As a promising implementation of Information-Centric Networking (ICN), Named Data Networking (NDN) has potential advantages over the TCP/IP network in content distribution, mobility support, etc. However, the research on NDN is still in its infancy, and congestion control, NDN's most important functional element, poses many challenges, such as congestion detection, excessive window reduction for non-congested paths, and unfairness. In this paper, we propose an Intelligent Edge-Aided Congestion Control (IEACC) scheme for the NDN network based on Deep Reinforcement Learning (DRL). The proposed IEACC provides a proactive congestion detector that utilizes intermediate routers to transmit accurate congestion information along the path to consumers through data packets. Furthermore, considering the multi-source transmission in NDN, IEACC divides data packets into different congestion degrees by a lightweight clustering algorithm and provides suitable inputs for DRL, thereby obtaining a reasonable transmission rate. Then, it distributes the estimated bandwidth resources to consumers with transmission needs to maintain fairness. Finally, we implement our proposed scheme in the simulation platform and evaluate the performance in different scenarios. The results show that it can improve data transmission rate, reduce packet loss, and maintain fairness compared with others.
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ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2022.3196344