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Title
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Towards information-centric edge platform for mesh networks : the case of CityLab Testbed
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Author
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Abstract
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| By leveraging resources from the Fed4Fire+ City-Lab testbed, we design the PiGeon edge computing platform that experiments solution that enable ICN based edge services in wireless mesh networks (WMNs). PiGeon combines into a platform several trends in edge computing namely the ICN (Information-Centric Networking), the containerization of services exemplified by Docker, novel service placement algorithms and the increasing availability of energy efficient but still powerful hardware at user premises (Raspberry Pi, mini-PCs, and enhanced home gateways). We underpin the PiGeon platform with Docker container-based service that can be seamlessly delivered, cached and deployed at the network edge. The core of the PiGeon platform is the Decision Engine making a decision on where and when to deploy a service instance to satisfy the service requirements while considering the network status and available hardware resources. We collect network data from a real citywide mesh network such as CityLab FIRE testbed located at the city of Antwerp, Belgium. The collected data is used to feed our service placement heuristic within the PiGeon platform. Through a real deployment in CityLab testbed, we show that our service placement heuristic improves the response time up to 37% for stateful services (Web2.0 service). Apart from improving the QoS for end-users, our results show that ICN plays a key role in improving the service delivery time as well as reducing the traffic consumption in WMNs. The overall effect of ICN in our platform is that most content and service delivery requests can be satisfied very close to the client device, many times just one hop away, decoupling QoS from intra-network traffic and origin server load. |
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Language
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English
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Source (book)
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2020 IEEE International Conference on Fog Computing (ICFC), 21-24 April, 2020, Sydney, Australia
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Publication
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IEEE
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2020
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ISBN
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978-1-72811-086-8
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DOI
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10.1109/ICFC49376.2020.00016
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Volume/pages
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p. 50-55
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ISI
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000589694600007
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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