5G-ICN : Delivering ICN Services over 5G using Network Slicing
October 04, 2016 Β· Declared Dead Β· π IEEE Communications Magazine
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ravishankar Ravindran, Asit Chakraborti, Syed Obaid Amin, Aytac Azgin, Guoqiang Wang
arXiv ID
1610.01182
Category
cs.NI: Networking & Internet
Citations
113
Venue
IEEE Communications Magazine
Last Checked
4 months ago
Abstract
The challenging requirements of 5G--from both the applications and the architecture perspectives--motivate the need to explore the feasibility of delivering services over new network architectures. As 5G proposes application-centric network slicing, which enables the use of new data planes realizable over a programmable compute, storage, and transport infrastructure, we consider Information-centric Networking (ICN) as a candidate network architecture to realize 5G objectives. This can co-exist with end-to-end IP services that are offered today. To this effect, we first propose a 5G-ICN architecture and compare its benefits (i.e., innovative services offered by leveraging ICN features) to current 3GPP-based mobile architectures. We then introduce a general application-driven framework that emphasizes on the flexibility afforded by Network Function Virtualization (NFV) and Software Defined Networking (SDN) over which 5G-ICN can be realized. We specifically focus on the issue of how mobility-as-a-service (MaaS) can be realized as a 5G-ICN slice, and give an in-depth overview on resource provisioning and inter-dependencies and -coordinations among functional 5G-ICN slices to meet the MaaS objectives.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted