scholarly journals Popularity-Aware In-Network Caching for Edge Named Data Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiliang Yin ◽  
Congfeng Jiang ◽  
Hidetoshi Mino ◽  
Christophe Cérin

The traditional centralized network architecture can lead to a bandwidth bottleneck in the core network. In contrast, in the information-centric network, decentralized in-network caching can alleviate the traffic flow pressure from the network center to the edge. In this paper, a popularity-aware in-network caching policy, namely, Pop, is proposed to achieve an optimal caching of network contents in the resource-constrained edge networks. Specifically, Pop senses content popularity and distributes content caching without adding additional hardware and traffic overhead. We conduct extensive performance evaluation experiments by using ndnSIM. The experiments showed that the Pop policy achieves 54.39% cloud service hit reduction ratio and 22.76% user request average hop reduction ratio and outperforms other policies including Leave Copy Everywhere, Leave Copy Down, Probabilistic Caching, and Random choice caching. In addition, we proposed an ideal caching policy (Ideal) as a baseline whose popularity is known in advance; the gap of Pop and Ideal in cloud service hit reduction ratio is 4.36%, and the gap in user request average hop reduction ratio is only 1.47%. More simulation results further show the accuracy of Pop in perceiving popularity of contents, and Pop has good robustness in different request scenarios.

2019 ◽  
Author(s):  
Rajavelsamy R ◽  
Debabrata Das

5G promises to support new level of use cases that will deliver a better user experience. The 3rd Generation Partnership Project (3GPP) [1] defined 5G system introduced fundamental changes on top of its former cellular systems in several design areas, including security. Unlike in the legacy systems, the 5G architecture design considers Home control enhancements for roaming customer, tight collaboration with the 3rd Party Application servers, Unified Authentication framework to accommodate various category of devices and services, enhanced user privacy, and secured the new service based core network architecture. Further, 3GPP is investigating the enhancements to the 5G security aspects to support longer security key lengths, False Base station detection and wireless backhaul in the Phase-2 of 5G standardization [2]. This paper provides the key enhancements specified by the 3GPP for 5G system, particularly the differences to the 4G system and the rationale behind the decisions.


2019 ◽  
Vol 11 (9) ◽  
pp. 193 ◽  
Author(s):  
Florian Völk ◽  
Konstantinos Liolis ◽  
Marius Corici ◽  
Joe Cahill ◽  
Robert T. Schwarz ◽  
...  

The 5G vision embraces a broad range of applications including the connectivity in underserved and remote areas. In particular, for these applications, satellites are going to play a role in future 5G networks to provide capacity on trains, vessels, aircraft, and for base stations around the globe. In this paper, a 5G edge node concept, developed and evaluated with over-the-air tests using satellites in the geostationary orbit, is presented. The article covers a testbed demonstration study in Europe with a large-scale testbed including satellites and the latest standardization for the network architecture. The main goal of this testbed is to evaluate how satellite networks can be best integrated within the convergent 5G environment. The over-the-air tests for 5G satellite integration in this article are based on a 3GPP Release 15 core network architecture.


Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Yantong Wang ◽  
Vasilis Friderikos

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching.


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