Analysis and implementation: converging intent based production and high speed research networks
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Date
2019
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Montana State University - Bozeman, College of Engineering
Abstract
This thesis analyzes the performance impact of converging an intent-based production network with a high-performance research network. The previous solution for high performance research networks was to segregate these networks [1]. This solution created a physically separate network dedicated to the researcher and used for high speed data transmissions. This model has been successfully utilized for years however current refresh cycles are force academic institutions to confront the ongoing maintenance of these boutique networks. Some institutions have noted their investment in their production networks have created performance to rival that of the dedicated network. For these institutions convergence to one network proves to be a viable strategic option. Additionally, vendors are going to market with intent based or software defined networking which answers many of the challenges that required the physical separation of networks. The advantages of both converged networks as well as software defined networks are well documented. Both campus Information Technology departments and the researches with this high-performance needs are in need of a quantitative analysis to understand the performance or security trade-offs associated with moving research onto a production, intent-based network. This thesis addresses this question by measuring and comparing key performance metrics of a traditional high-performance research network, a traditional production network, and a converged Intent-Based network in the same three labs at the same institution (Montana State University). The results prove that a converged, intent based network delivers the same (or superior) performance as the previous model with the same or superior level of segregation (security). These results give institutions the ability to shed the traditional, utilitarian use of institutional networks in favor of a dynamic network model based on the identity and use of the network rather than the physical location.