Vicinity-based Replica Finding in Named Data Networking

Suwannasa, A. and Broadbent, M. and Mauthe, A. (2020) Vicinity-based Replica Finding in Named Data Networking. In: 2020 International Conference on Information Networking (ICOIN). IEEE, pp. 146-151. ISBN 9781728142005

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Abstract

In Named Data Networking (NDN) architectures, a content object is located according to the content's identifier and can be retrieved from all nodes that hold a replica of the content. The default forwarding strategy of NDN is to forward an Interest packet along the default path from the requester to the server to find a content object according to its name prefix. However, the best path may not be the default path, since content might also be located nearby. Hence, the default strategy could result in a sub-optimal delivery efficiency. To address this issue we introduce a vicinity-based replica finding scheme. This is based on the observation that content objects might be requested several times. Therefore, replicas can be often cached within a particular neighbourhood and thus it might be efficient to specifically look for them in order to improve the content delivery performance. Within this paper, we evaluate the optimal size of the vicinity within which content should be located (i.e. the distance between the requester and its neighbours that are considered within the content search). We also compare the proposed scheme with the default NDN forwarding strategy with respect to replica finding efficiency and network overhead. Using the proposed scheme, we demonstrate that the replica finding mechanism reduces the delivery time effectively with acceptable overhead costs.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
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Subjects:
?? CONTENT DELIVERY PERFORMANCECONTENT SEARCHFINDING EFFICIENCYFORWARDING STRATEGIESNAMED DATA NETWORKINGSNEIGHBOURHOODNETWORK OVERHEADOVERHEAD COSTSEFFICIENCY ??
ID Code:
142925
Deposited By:
Deposited On:
28 Apr 2020 12:55
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2023 02:29