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Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

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Abstract

Modern network services make increasing use of virtualized compute and network resources. This is enabled by the growing availability of softwarized network functions, which take on major roles in the total traffic flow (such as caching, routing or as firewall). To ensure reliable operation of its services, the service provider needs a good understanding of the performance of the deployed softwarized network functions. Ideally, the service performance should be predictable, given a certain input workload and a set of allocated (virtualized) resources (such as vCPUs and bandwidth). This helps to estimate more accurately how much resources are needed to operate the service within its performance specifications. To predict its performance, the network function should be profiled in the whole range of possible input workloads and resource configurations. However, this input can span a large space of multiple parameters and many combinations to test, resulting in an expensive and overextended measurement period. To mitigate this, we present a profiling framework and a sampling heuristic to help select both workload and resource configurations to test. Additionally, we compare several machine-learning based methods for the best prediction accuracy, in combination with the sampling heuristic. As a result, we obtain a reduced dataset which can still model the performance of the network functions with adequate accuracy, while requiring less profiling time. Compared to uniform sampling, our tests show that the heuristic achieves the same modeling accuracy with up to five times less samples.

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Notes

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Acknowledgements

This work has been performed in the framework of the NGPaaS and 5GTANGO project, funded by the European Commission under the Horizon 2020 and 5G-PPP Phase2 programmes, resp. under Grant Agreement No. 761557 and 761493 (http://ngpaas.eu) (https://www.5gtango.eu). This work is partly funded by UGent BOF/GOA project “Autonomic Networked Multimedia Systems”

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  1. IDLab, iGent Tower-Department of Information Technology, Ghent University-imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Belgium

    Steven Van Rossem, Wouter Tavernier, Didier Colle, Mario Pickavet & Piet Demeester

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  1. Steven Van Rossem
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Correspondence to Steven Van Rossem.

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Van Rossem, S., Tavernier, W., Colle, D. et al. Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions. J Netw Syst Manage 28, 1482–1521 (2020). https://doi.org/10.1007/s10922-020-09547-8

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