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Automised flow rule formation by using machine learning in software defined networks based edge computing

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journal contribution
posted on 2022-03-23, 08:46 authored by Saleem Iqbal, Hira Maryam, Kashif Naseer Qureshi, Ibrahim Tariq Javed, Noel Crespi
The availability of Software Defined Network’s (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wild-card rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane’s response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement.

Funding

Study on Aerodynamic Characteristics Control of Slender Body Using Active Flow Control Technique

Japan Society for the Promotion of Science

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History

Publication

Egyptian Informatics Journal;23, pp. 149–157

Publisher

Elsevier

Note

peer-reviewed

Other Funding information

SFI, Horizon 2020, ERC, ERC

Language

English

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