Design and Implementation of an Open Source Framework and Prototype For Named Data Networking-Based Edge Cloud Computing System
- Authors
- Ullah, Rehmat; Rehman, Muhammad Atif Ur; Kim, Byung-Seo
- Issue Date
- 2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Internet of things; edge computing; fog computing; distributed computing; caching; named data networking; framework; offloading; latency; scalability; multi-access edge computing; future Internet
- Citation
- IEEE ACCESS, v.7, pp.57741 - 57759
- Journal Title
- IEEE ACCESS
- Volume
- 7
- Start Page
- 57741
- End Page
- 57759
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/2764
- DOI
- 10.1109/ACCESS.2019.2914067
- ISSN
- 2169-3536
- Abstract
- Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier 1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (loadbased lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.
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Collections - Graduate School > Software and Communications Engineering > 1. Journal Articles
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