Communication networks have not only become a critical infrastructure of our digital society, but are also increasingly complex and hence error-prone. This has recently motivated the study of more automated and “self-driving” networks: networks which measure, analyze, and control themselves in an adaptive manner, reacting to changes in the environment. In particular, such networks hence require a mechanism to recognize potential performance issues. This paper presents NetBOA, an adaptive and “data-driven” approach to measure network performance, allowing the network to identify bottlenecks and to perform automated what-if analysis, exploring improved network configurations. As a case study, we demonstrate how the NetBOA approach can be used to benchmark a popular software switch, Open vSwitch. We report on our implementation and evaluation, and show that NetBOA can find performance issues efficiently, compared to a non-data-driven approach. Our results hence indicate that NetBOA may also be useful to identify algorithmic complexity attacks.

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