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.

中文翻译：

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对虚拟化网络功能的性能进行建模的优化抽样策略

现代网络服务越来越多地使用虚拟化计算和网络资源。这是由于软件化网络功能的可用性不断增加而实现的，这些功能在总流量中扮演着主要角色（例如缓存、路由或防火墙）。为了确保其服务的可靠运行，服务提供商需要很好地了解部署的软件化网络功能的性能。理想情况下，给定一定的输入工作负载和一组分配的（虚拟化）资源（例如 vCPU 和带宽），服务性能应该是可预测的。这有助于更准确地估计在其性能规格内运行服务所需的资源量。为了预测它的性能，网络功能应该在可能的输入工作负载和资源配置的整个范围内进行分析。然而，该输入可能跨越多个参数和许多组合的大空间进行测试，从而导致昂贵且过度延长的测量周期。为了缓解这种情况，我们提出了一个分析框架和一个采样启发式方法，以帮助选择要测试的工作负载和资源配置。此外，我们将几种基于机器学习的方法与采样启发式相结合，以获得最佳预测精度。结果，我们获得了一个简化的数据集，该数据集仍然可以以足够的精度对网络功能的性能进行建模，同时需要更少的分析时间。与均匀采样相比，