Design of software-defined network experimental teaching scheme based on virtualised Environment
Aiming to address the shortage of experimental resources, the high cost of large-scale deployment of hardware experimental environment and the difficulty for students to get started in the software-defined network (SDN) course, this article proposes an SDN experimental teaching scheme based on the v...
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Published in | Applied mathematics and nonlinear sciences Vol. 6; no. 2; pp. 181 - 192 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Sciendo
01.07.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Aiming to address the shortage of experimental resources, the high cost of large-scale deployment of hardware experimental environment and the difficulty for students to get started in the software-defined network (SDN) course, this article proposes an SDN experimental teaching scheme based on the virtualised environment, and gives a specific experimental scheme design. The scheme utilises virtualisation technology to build a SDN experimental environment quickly, uses a lightweight network simulation platform – that goes by the name of Mininet – to build the SDN network and uses open-source controller Floodlight for centralised control of the SDN network. The scheme is mainly divided into three phases: basic, improvement and synthesis. In the basic phase, experimental projects mainly include the study of SDN basic concepts and the use of relevant tools; in the improvement phase, experimental projects mainly include the use of SDN flow table, group table, etc; in the synthetic phase, we design two innovative experimental projects that use computational intelligence technology to achieve efficient load balancing and accurate malicious attack detection. The difficulty of each phase is increasing. The constantly evolving levels of difficulty allow the individual needs of students with different levels to be met, thereby improving the effect of SDN experimental teaching and cultivating innovative SDN talents. |
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ISSN: | 2444-8656 2444-8656 |
DOI: | 10.2478/amns.2021.2.00005 |