Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions
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Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions Steven Van Rossem1 · Wouter Tavernier1 · Didier Colle1 · Mario Pickavet1 · Piet Demeester1 Received: 20 January 2020 / Revised: 25 May 2020 / Accepted: 10 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Sampling heuristic · Network Function Virtualization · Performance profiling · Machine learning · Regression
* Steven Van Rossem [email protected] Extended author information available on the last page of the article
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Vol.:(0123456789)
Journal of Network and Systems Management
1 Introduction In the telecom industry, there is an increasing adoption of cloud-native services and network functions based on Software Defined Networking (SDN) and Network Function Virtualization (NFV) techniques. By virtualizing compute and network resources, a very flexible environment can be created to deploy Virtual Network Functions (VNFs) with an optimal amount of allocated resources, adapted to the realtime incoming workload. The recent rise of 5G enabled services further advocates the use of cloud-native functions, which are deployed over a virtualized infrastructure [1, 2]. This illustrates the growing need to map the amount of allocated resources and incoming workload to the Key Performance Indicators (KPIs) of the deployed network service, specified in the Service Level Agreement (SLA). To characte
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