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The future of networking is the future of big data

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dc.contributor.authorShannigrahi, Susmit, author
dc.contributor.authorPapadopoulos, Christos, advisor
dc.contributor.authorPartridge, Craig, advisor
dc.contributor.authorPallickara, Shrideep, committee member
dc.contributor.authorRay, Indrakshi, committee member
dc.contributor.authorBurns, Patrick J., committee member
dc.contributor.authorMonga, Inder, committee member
dc.date.accessioned2019-09-10T14:35:49Z
dc.date.available2019-09-10T14:35:49Z
dc.date.available2019-09-10T14:35:49Z
dc.date.issued2019
dc.description.abstractScientific domains such as Climate Science, High Energy Particle Physics (HEP), Genomics, Biology, and many others are increasingly moving towards data-oriented workflows where each of these communities generates, stores and uses massive datasets that reach into terabytes and petabytes, and projected soon to reach exabytes. These communities are also increasingly moving towards a global collaborative model where scientists routinely exchange a significant amount of data. The sheer volume of data and associated complexities associated with maintaining, transferring, and using them, continue to push the limits of the current technologies in multiple dimensions - storage, analysis, networking, and security. This thesis tackles the networking aspect of big-data science. Networking is the glue that binds all the components of modern scientific workflows, and these communities are becoming increasingly dependent on high-speed, highly reliable networks. The network, as the common layer across big-science communities, provides an ideal place for implementing common services. Big-science applications also need to work closely with the network to ensure optimal usage of resources, intelligent routing of requests, and data. Finally, as more communities move towards data-intensive, connected workflows - adopting a service model where the network provides some of the common services reduces not only application complexity but also the necessity of duplicate implementations. Named Data Networking (NDN) is a new network architecture whose service model aligns better with the needs of these data-oriented applications. NDN's name based paradigm makes it easier to provide intelligent features at the network layer rather than at the application layer. This thesis shows that NDN can push several standard features to the network. This work is the first attempt to apply NDN in the context of large scientific data; in the process, this thesis touches upon scientific data naming, name discovery, real-world deployment of NDN for scientific data, feasibility studies, and the designs of in-network protocols for big-data science.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierShannigrahi_colostate_0053A_15549.pdf
dc.identifier.urihttps://hdl.handle.net/10217/197325
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rights.licenseThis material is open access and distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectfuture internet architecture
dc.subjectlarge scientific data
dc.subjectnetworking for big data
dc.subjectinformation centric networking
dc.subjectbig science
dc.subjectnamed data networking
dc.titleThe future of networking is the future of big data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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