A Congestion Control Method for Named Data Networking with Hop-by-Hop Window-Based Approach

2019 ◽  
Vol E102.B (1) ◽  
pp. 97-110 ◽  
Author(s):  
Takahiko KATO ◽  
Masaki BANDAI ◽  
Miki YAMAMOTO
Keyword(s):  
Congestion Control ◽  
Control Method ◽  
Named Data Networking ◽  
Data Networking

scholarly journals Intelligent Forwarding Strategy for Congestion Control Using Q-Learning and LSTM in Named Data Networking

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Sanguk Ryu ◽  
Inwhee Joe ◽  
WonTae Kim
Keyword(s):  
Congestion Control ◽  
Network Architecture ◽  
Short Term Memory ◽  
Control Method ◽  
Second Phase ◽  
Named Data Networking ◽  
Entry Rate ◽  
Q Learning ◽  
Forwarding Strategy ◽  
Data Networking

Named data networking (NDN) is a future network architecture that replaces IP-oriented communication with content-oriented communication and has new features such as cache, multiple paths, and multiple sources. Services such as video streaming, to which NDN can be applied in the future, can cause congestion if data is concentrated on one of the nodes during high demand. To solve this problem, sending rate control methods such as TCP congestion control have been proposed, but they do not adequately reflect the characteristics of NDN. Therefore, we use reinforcement learning and deep learning to propose a congestion control method that takes advantage of multipath features. The intelligent forwarding strategy for congestion control using Q-learning and long short-term memory in NDN proposed in this paper is divided into two phases. The first phase uses an LSTM model to train a pending interest table (PIT) entry rate that can be used as an indicator to detect congestion by knowing the amount of data returned. In the second phase, it is forwarded to an alternative path that is not congestive via Q-learning based on the PIT entry rate predicted by the trained LSTM model. The simulation results show that the proposed method increases the data reception rate by 6.5% and 19.5% and decreases the packet drop rate by 7.3% and 17.2% compared to an adaptive SRTT-based forwarding strategy (ASF) and BestRoute.

Countermeasures of interest flooding attack in named data networking: A survey

2021 ◽  
pp. 002072092098351
Author(s):  
Linjun Yu ◽  
Huali Ai ◽  
Dong-Oun Choi
Keyword(s):  
Control Method ◽  
Network Architectures ◽  
Security Threats ◽  
Named Data Networking ◽  
Comprehensive Understanding ◽  
Next Generation Internet ◽  
Flooding Attack ◽  
Token Bucket ◽  
User Reputation ◽  
Data Networking

Named data networking (NDN) is a typical representation and implementation of information-centric networking and serves as a basis for the next-generation Internet. However, any network architectures will face information security threats. An attack named interest flooding attack (IFA), which is evolved, has becomes a great threat for NDN in recent years. Attackers through insert numerous forged interest packets into an NDN network, making the cache memory of NDN router(s) overrun, interest packets for the intended users. To take a comprehensive understanding of recent IFA detection and mitigation approaches, in this paper, we compared nine typical approaches to resolving IFA attacks for NDN, which are interest traceback, token bucket with per interface fairness, satisfaction-based interest acceptance, satisfaction-based push back, disabling PIT exhaustion, interest flow control method based on user reputation and content name prefixes, interest flow balancing method focused on the number of requests on named data networking, cryptographic route token, Poseidon local, and Poseidon distributed techniques. In addition, we conducted a simulation using Poseidon, a commonly used IFA resolution approach. The results showed that Poseidon could resolve IFA issues effectively.

ACCP: adaptive congestion control protocol in named data networking based on deep learning

2018 ◽  
Vol 31 (9) ◽  
pp. 4675-4683 ◽  
Author(s):  
Tingting Liu ◽  
Mingchuan Zhang ◽  
Junlong Zhu ◽  
Ruijuan Zheng ◽  
Ruoshui Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Congestion Control ◽  
Named Data Networking ◽  
Control Protocol ◽  
Adaptive Congestion Control ◽  
Data Networking
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