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https://dx.doi.org/10.24442/bo...
Other literature type . 2017
Data sources: Datacite
https://dx.doi.org/10.7892/bor...
Doctoral thesis . 2024
Data sources: Datacite
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Network Coding Enabled Named Data Networking Architectures

Authors: Saltarin de Arco, Jonnahtan Eduardo;

Network Coding Enabled Named Data Networking Architectures

Abstract

The volume of data traffic in the Internet has increased drastically in the last years, mostly due to data intensive applications like video streaming, file sharing, etc.. This motivates the development of new communication methods that can deal with the growing volume of data traffic. To this aim, Named Data Networking (NDN) has been proposed as a future Internet architecture that changes how the Internet works, from the exchange of content between particular nodes of the network, to retrieval of particular content in the network. The NDN architecture enables ubiquitous in-network caching and naturally supports dynamic selection of content sources, characteristics that fit well with the communication needs of data intensive applications. However, the performance of data intensive applications is degraded by the limited throughput seen by applications, which can be caused by (i) limited bandwidth, (ii) network bottlenecks and (iii) packet losses. In this thesis, we argue that introducing network coding into the NDN architecture improves the performance of NDN-based data intensive applications by alleviating the three issues presented above. In particular, network coding (i) enables efficient multipath data retrieval in NDN, which allows nodes to aggregate all the bandwidth available through their multiple interfaces; (ii) allows information from multiple sources to be combined at the intermediate routers, which alleviates the impact of network bottlenecks; and (iii) enables clients to efficiently handle packet losses. This thesis first provides an architecture that enables network coding in NDN for data intensive applications. Then, a study demonstrates and quantifies the benefits that network coding brings to video streaming over NDN, a particular data intensive application. To study the benefits that network coding brings in a more realistic NDN scenario, this thesis finally provides a caching strategy that is used when the in-network caches have limited capacity. Overall, the evaluation results show that the use of network coding permits to exploit more efficiently available network resources, which leads to reduced data traffic load on the sources, increased cache-hit rate at the in-network caches and faster content retrieval at the clients. In particular, for video streaming applications, network coding enables clients to watch higher quality videos compared to using traditional NDN, while it also reduces the video servers' load. Moreover, the proposed caching strategy for network coding enabled NDN maintains the benefits that network coding brings to NDN even when the caches have limited storage space.

Country
Switzerland
Related Organizations
Keywords

knowledge & systems, 510 Mathematics, 000 Computer science, 000 Computer science, knowledge & systems, 000 Computer science, knowledge & systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green