Pseudo-LRU Replacement Policy in Named Data Networking Using Fat Tree DataCenter Network Architecture

2017 ◽  
Author(s):  
Imen Dhiab ◽  
Yosra Barouni ◽  
Sofiane Khalfallah ◽  
Jaleleddine Ben Hadj Slama
Keyword(s):  
Network Architecture ◽  
Replacement Policy ◽  
Named Data Networking ◽  
Datacenter Network ◽  
Data Networking

scholarly journals SIMULASI NDN MENGGUNAKAN NDNSIM PADA JARINGAN INTRANET KAMPUS TEKNOLOGI INFORMASI FAKULTAS TEKNIK UNIVERSITAS UDAYANA

2019 ◽  
Vol 2 (2) ◽  
pp. 136-140
Author(s):  
I Putu Agus Eka Pratama ◽  
Mohammad Ernico Suryo Wicaksono
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Network Architecture ◽  
New Technology ◽  
Named Data Networking ◽  
Internet Technologies ◽  
Initial Testing ◽  
Destination Address ◽  
Data Networking

NDN (Named Data Networking) is one of today's internet technologies with identifier of the packet is given in the form of a content name, not a source or destination address. Such properties make NDN have a new forwarding mechanism and different from host to host (IP-based) network architecture. Utilization of this new technology is usually incomplete or there is a need for simulations for initial testing and testing. This paper will discuss about NDN simulation. The simulation will be done using ndnSIM, an open source simulator and testing was performed at Information Technology Campus, Faculty of Engineering, Udayana University’s intranet.

scholarly journals Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking

2019 ◽  
Vol 11 (6) ◽  
pp. 139 ◽  
Author(s):  
Meng Kuai ◽  
Xiaoyan Hong
Keyword(s):  
Data Acquisition ◽  
Autonomous Vehicles ◽  
Network Architecture ◽  
Large Scale ◽  
Ad Hoc ◽  
Named Data Networking ◽  
Global Knowledge ◽  
Broadcast Storm ◽  
Broadcast Schemes ◽  
Data Networking

The emerging connected and autonomous vehicles (CAVs) challenge ad hoc wireless multi-hop communications by mobility, large-scale, new data acquisition and computing patterns. The Named Data Networking (NDN) is suitable for such vehicle ad hoc networks due to its information centric networking approach. However, flooding interest packets in ad-hoc NDN can lead to broadcast storm issue. Existing solutions will either increase the number of redundant interest packets or need a global knowledge about data producers. In this paper, a Location-Based Deferred Broadcast (LBDB) scheme is introduced to improve the efficiency and performance of interest broadcast in ad-hoc NDN. The scheme takes advantage of location information to set up timers when rebroadcasting an interest. The LBDB is implemented in V-NDN network architecture using ndnSIM simulator. Comparisons with several existing protocols are conducted in simulation. The results show that LBDB improves the overhead, the average number of hops and delay while maintaining an average satisfaction ratio when compared with several other broadcast schemes. The improvement can help offer timely data acquisition for quick responses in emergent CAV application situations.

scholarly journals End-to-End Networks Vs Named Data Network: A Critical Evaluation

2015 ◽  
Vol 72 (5) ◽  
Author(s):  
Suhaidi Hassan ◽  
Walid Elbreiki ◽  
Mohamed Firdhous ◽  
Adib M. Monzer Habbal
Keyword(s):  
Network Architecture ◽  
Critical Evaluation ◽  
Graphical Form ◽  
The Internet ◽  
Named Data Networking ◽  
Simulated Environment ◽  
End To End ◽  
The Comparative Study ◽  
Networking Architectures ◽  
Data Networking

Named data networking or information centric networking is the newest networking paradigm that gives foremost place to the contents in identification and dissemination. On the other hand, the end to end networking paradigm on which the Internet is currently built on places heavy emphasis on devices that make the architecture. The current Internet suffers from many shortcomings due to the misplaced emphasis. In order to overcome some of these deficiencies, researchers and developers have come up with patches and work around that have made the Internet more complex than it ought to be. Named data networking is a clean slate approach in building a network architecture overcoming all the current deficiencies and make it future safe. Several researchers have carried out comparative studies between named data networking and end to end networking. But these studies concentrate only on the features and capabilities of the networking paradigms. This is the first attempt at quantifying the performance the networking architectures experimentally. The authors in this paper present the results of the comparative study carried out experimentally in a simulated environment based on the final throughput. The results have been presented in a graphical form for easy visualization of results.

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.

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