Local Tree Hunting: Finding Closest Contents from In-Network Cache

H Shimizu, H Asaeda, M Jibiki… - … on Information and …, 2015 - search.ieice.org
H Shimizu, H Asaeda, M Jibiki, N Nishinaga
IEICE TRANSACTIONS on Information and Systems, 2015search.ieice.org
How to retrieve the closest content from an in-network cache is one of the most important
issues in Information-Centric Networking (ICN). This paper proposes a novel content
discovery scheme called Local Tree Hunting (LTH). By adding branch-cast functionality to a
local tree for content requests to a Content-Centric Network (CCN) response node, the
discovery area for caching nodes expands. Since the location of such a branch-casting node
moves closer to the request node when the content is more widely cached, the discovery …
How to retrieve the closest content from an in-network cache is one of the most important issues in Information-Centric Networking (ICN). This paper proposes a novel content discovery scheme called Local Tree Hunting (LTH). By adding branch-cast functionality to a local tree for content requests to a Content-Centric Network (CCN) response node, the discovery area for caching nodes expands. Since the location of such a branch-casting node moves closer to the request node when the content is more widely cached, the discovery range, i.e. the branch size of the local tree, becomes smaller. Thus, the discovery area is autonomously adjusted depending on the content dissemination. With this feature, LTH is able to find the “almost true closest” caching node without checking all the caching nodes in the in-network cache. The performance analysis employed in Zipf's law content distribution model and which uses the Least Recently Used eviction rule shows the superiority of LTH with respect to identifying the almost exact closest cache.
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