Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/79557

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dc.contributor.authorSilva, Elídio Tomás dapor
dc.contributor.authorMacedo, Joaquimpor
dc.contributor.authorCosta, Antóniopor
dc.date.accessioned2022-09-16T14:47:52Z-
dc.date.available2022-09-16T14:47:52Z-
dc.date.issued2022-03-04-
dc.identifier.citationSilva, E.T.d.; Macedo, J.M.H.d.; Costa, A.L.D. NDN Content Store and Caching Policies: Performance Evaluation. Computers 2022, 11, 37. https://doi.org/10.3390/computers11030037por
dc.identifier.issn2073-431Xpor
dc.identifier.urihttps://hdl.handle.net/1822/79557-
dc.description.abstractAmong various factors contributing to performance of named data networking (NDN), the organization of caching is a key factor and has benefited from intense studies by the networking research community. The performed studies aimed at (1) finding the best strategy to adopt for content caching; (2) specifying the best location, and number of content stores (CS) in the network; and (3) defining the best cache replacement policy. Accessing and comparing the performance of the proposed solutions is as essential as the development of the proposals themselves. The present work aims at evaluating and comparing the behavior of four caching policies (i.e., random, least recently used (LRU), least frequently used (LFU), and first in first out (FIFO)) applied to NDN. Several network scenarios are used for simulation (2 topologies, varying the percentage of nodes of the content stores (5–100), 1 and 10 producers, 32 and 41 consumers). Five metrics are considered for the performance evaluation: cache hit ratio (CHR), network traffic, retrieval delay, interest re-transmissions, and the number of upstream hops. The content request follows the Zipf–Mandelbrot distribution (with skewness factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi><mo>=</mo><mn>1.1</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi><mo>=</mo><mn>0.75</mn></mrow></semantics></math></inline-formula>). LFU presents better performance in all considered metrics, except on the NDN testbed, with 41 consumers, 1 producer and a content request rate of 100 packets/s. For the level of content store from 50% to 100%, LRU presents a notably higher performance. Although the network behavior is similar for both skewness factors, when <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi><mo>=</mo><mn>0.75</mn></mrow></semantics></math></inline-formula>, the CHR is significantly reduced, as expected.por
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.por
dc.language.isoengpor
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)por
dc.relationUIDB/00319/2020por
dc.rightsopenAccesspor
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/por
dc.subjectCaching replacement policiespor
dc.subjectNamed data networkingpor
dc.subjectPerformance evaluationpor
dc.titleNDN content store and caching policies: performance evaluationpor
dc.typearticlepor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://www.mdpi.com/2073-431X/11/3/37por
oaire.citationStartPage1por
oaire.citationEndPage17por
oaire.citationIssue3por
oaire.citationVolume11por
dc.date.updated2022-03-24T14:47:31Z-
dc.identifier.eissn2073-431X-
dc.identifier.doi10.3390/computers11030037por
dc.subject.wosScience & Technologypor
sdum.journalComputerspor
oaire.versionVoRpor
dc.identifier.articlenumber37por
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