Centrality-Based Network Coder Placement for Peer-To-Peer Content Distribution

Nguyen, Dinh C.; Nakazato, Hidenori

doi:10.5121/ijcnc.2013.5312

Cited by 5 publications

(3 citation statements)

References 31 publications

(50 reference statements)

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“…In a similar context, Yufei et al [15] use "control nodes" to cache content based on betweenness centrality. Nguyen and Nakazato [16] proposed a betweenness-centralitybased network coder placement for peer-to-peer (P2P) content distribution. However, they do not consider content placement; they use random linear network coding at every coding node to assist content delivery.…”

Section: Related Workmentioning

confidence: 99%

LoCHiP: A Distributed Collaborative Cache Management Scheme at the Network Edge

Khan

Westphal

Garcia-Luna-Aceves

et al. 2020

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

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Using local caches is becoming a necessity to alleviate bandwidth pressure on cellular links, and a number of caching approaches advocate caching popular content at nodes with high centrality, which quantifies how well connected nodes are. These approaches have been shown to outperform caching policies unrelated to node connectivity. However, caching content at highly connected nodes places poorly connected nodes with low centrality at a disadvantage: in addition to their poor connectivity, popular content is placed far from them at the more central nodes. We propose reversing the way in which node connectivity is used for the placement of content in caching networks, and introduce a Low-Centrality High-Popularity (LoCHiP) caching algorithm that populates poorly connected nodes with popular content. We conduct a thorough evaluation of LoCHiP against other centrality-based caching policies and traditional caching methods using hit rate, and hop-count to content as performance metrics. The results show that LoCHiP outperforms significantly the other methods.

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Section: Related Workmentioning

confidence: 99%

LoCHiP: A Distributed Collaborative Cache Management Scheme at the Network Edge

Khan

Westphal

Garcia-Luna-Aceves

et al. 2020

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

View full text Add to dashboard Cite

show abstract

“…In a similar context, Yufei et al [15] use "control nodes" to cache content based on betweeness centrality. Nguyen and Nakazato [16] proposed a betweenness-centrality-based network coder placement for peer-to-peer (P2P) content distribution. However, they do not consider content placement; they use random linear network coding at every coding node to assist content delivery.…”

Section: Related Workmentioning

confidence: 99%

Reversing The Meaning of Node Connectivity for Content Placement in Networks of Caches

Khan

Westphal

Garcia-Luna-Aceves

et al. 2020

2020 International Conference on Computing, Networking and Communications (ICNC)

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It is a widely accepted heuristic in content caching to place the most popular content at the nodes that are the best connected. The other common heuristic is somewhat contradictory, as it places the most popular content at the edge, at the caching nodes nearest the users. We contend that neither policy is best suited for caching content in a network and propose a simple alternative that places the most popular content at the least connected node. Namely, we populate content first at the nodes that have the lowest graph centrality over the network topology. Here, we provide an analytical study of this policy over some simple topologies that are tractable, namely regular grids and trees. Our mathematical results demonstrate that placing popular content at the least connected nodes outperforms the aforementioned alternatives in typical conditions.

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“…The work of Li et al was followed by Ho et al [6] that showed that random linear combinations construct good network codes with high probability. Previous work [7] has powerfully indicated that using network coding in P2P file sharing system can effectively improve system throughput and handle dynamics of nodes in the network, including node arrival and node departure, node and piece selection, link failure, etc., thereby optimizing system performance. Therefore, we use (n, k) erasure code (a kind of network coding) in our file swarming protocol.…”

Section: Introductionmentioning

confidence: 99%