Enhancing Performance, Security, and Management in Network Function Virtualization

Yang Zhang, Zhi Li Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In an era of ubiquitous connectivity, various new applications, network protocols, and online services (e.g., cloud services, distributed machine learning, cryptocurrency) have been constantly creating, underpinning many of our daily activities. Emerging demands for networks have led to growing traffic volume and complexity of modern networks, which heavily rely on a wide spectrum of specialized network functions (e.g., Firewall, Load Balancer) for diverse purposes. Although these (virtual) network functions (VNFs) are widely deployed, they are instantiated in an uncoordinated manner failing to meet growing demands of evolving networks. In this dissertation, we argue that networks equipped with VNFs can be designed in a fashion similar to how computer software is programmed today. By following the blueprint of modularization, networks can be made more efficient, secure, and manageable.

Original languageEnglish (US)
Title of host publication2020 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2020 - Proceedings
EditorsLarry Horner, Kurt Tutschku, Antonio de la Oliva, Sandra Scott-Hayward, Marco Tacca, Georgiana Caltais, Helge Parzyjegla
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages126-131
Number of pages6
ISBN (Electronic)9781728181592
DOIs
StatePublished - Nov 10 2020
Event2020 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2020 - Virtual, Madrid, Spain
Duration: Nov 9 2020Nov 12 2020

Publication series

Name2020 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2020 - Proceedings

Conference

Conference2020 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2020
Country/TerritorySpain
CityVirtual, Madrid
Period11/9/2011/12/20

Bibliographical note

Funding Information:
This research was supported by various sources of funds: DoD ARO MURI Award W911NF-12-1-0385, DTRA grant HDTRA1-09-1-0050, NSF grants CNS-1411636, CNS-1618339, CNS-1617729, CNS 1814322 and CNS183677.

Publisher Copyright:
© 2020 IEEE.

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