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dc.contributor.advisorPartridge, Craig
dc.contributor.advisorPapadopoulos, Christos
dc.contributor.authorFan, Chengyu
dc.contributor.committeememberPallickara, Shrideep
dc.contributor.committeememberPallickara, Sangmi
dc.contributor.committeememberLuo, J. Rockey
dc.date.accessioned2020-06-22T11:53:59Z
dc.date.available2021-06-15T11:53:59Z
dc.date.issued2020
dc.description2020 Spring.
dc.descriptionIncludes bibliographical references.
dc.description.abstractNamed Data Networking (NDN) is a promising information-centric networking (ICN) Internet architecture that addresses the content directly rather than addressing servers. NDN provides new features, such as content-centric security, stateful forwarding, and in-network caches, to better satisfy the needs of today's applications. After many years of technological research and experimentation, the community has started to explore the deployment path for NDN. One NDN deployment challenge is measurement. Unlike IP, which has a suite of measurement approaches and tools, NDN only has a few achievements. NDN routing and forwarding are based on name prefixes that do not refer to individual endpoints. While rich NDN functionalities facilitate data distribution, they also break the traditional end-to-end probing based measurement methods. In this dissertation, we present our work to investigate NDN measurements and fill some research gaps in the field. Our thesis of this dissertation states that we can capture a substantial amount of useful and actionable measurements of NDN networks from end hosts. We start by comparing IP and NDN to propose a conceptual framework for NDN measurements. We claim that NDN can be seen as a superset of IP. NDN supports similar functionalities provided by IP, but it has unique features to facilitate data retrieval. The framework helps identify that NDN lacks measurements in various aspects. This dissertation focuses on investigating the active measurements from end hosts. We present our studies in two directions to support the thesis statement. We first present the study to leverage the similarities to replicate IP approaches in NDN networks. We show the first work to measure the NDN-DPDK forwarder, a high-speed NDN forwarder designed and implemented by the National Institute of Standards and Technology (NIST), in a real testbed. The results demonstrate that Data payload sizes dominate the forwarding performance, and efficiently using every fragment to improve the goodput. We then present the first work to replicate packet dispersion techniques in NDN networks. Based on the findings in the NDN-DPDK forwarder benchmark, we devise the techniques to measure interarrivals for Data packets. The results show that the techniques successfully estimate the capacity on end hosts when 1Gbps network cards are used. Our measurements also indicate the NDN-DPDK forwarder introduces variance in Data packet interarrivals. We identify the potential bottlenecks and the possible causes of the variance. We then address the NDN specific measurements, measuring the caching state in NDN networks from end hosts. We propose a novel method to extract fingerprints for various caching decision mechanisms. Our simulation results demonstrate that the method can detect caching decisions in a few rounds. We also show that the method is not sensitive to cross-traffic and can be deployed on real topologies for caching policy detection.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierFan_colostate_0053A_16038.pdf
dc.identifier.urihttps://hdl.handle.net/10217/208590
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020- CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.rights.accessEmbargo Expires: 06/15/2021
dc.subjectinformation centric networking
dc.subjectnetwork measurement
dc.subjectcaching
dc.subjectpacket dispersion techniques
dc.subjectnamed data networking
dc.titleMeasuring named data networks
dc.typeText
dcterms.embargo.expires2021-06-15
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)


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