Leveraging the potential of Virtualised Network Functions (VNFs) requires a clear understanding of the link between resource consumption and performance. The current state of the art tries to do that by utilising Machine Learning (ML) and specifically Supervised Learning (SL) models for given network environments and VNF types assuming single-objective optimisation targets. Taking a different approach poses a novel VNF profiler optimising multi-resource type allocation and performance objectives using adapted Reinforcement Learning (RL). Our approach can meet Key Performance Indicator (KPI) targets while minimising multi-resource type consumption and optimising the VNF output rate compared to existing single-objective solutions. Our experimental evaluation with three real-world VNF types over a total of 39 study scenarios (13 per VNF), for three resource types (virtual CPU, memory, and network link capacity), verifies the accuracy of resource allocation predictions and corresponding successful profiling decisions via a benchmark comparison between our RL model and SL models. We also conduct a complementary exhaustive search-space study revealing that different resources impact performance in varying ways per VNF type, implying the necessity of multi-objective optimisation, individualised examination per VNF type, and adaptable online profile learning, such as with the autonomous online learning approach of iOn-Profiler.

翻译：挖掘虚拟化网络功能（VNF）的潜力需要清晰理解资源消耗与性能之间的关联。当前领域的研究通过利用机器学习（ML），特别是针对特定网络环境和VNF类型的监督学习（SL）模型，并假设单目标优化目标。本文采用不同方法，提出一种基于自适应强化学习（RL）的新型VNF性能剖析器，可优化多资源类型分配与性能目标。与现有单目标解决方案相比，我们的方法能在最小化多资源类型消耗的同时满足关键绩效指标（KPI）目标，并优化VNF输出速率。我们针对三种真实VNF类型（每种13个场景，共39个研究场景）及虚拟CPU、内存和网络链路容量三种资源类型进行实验评估，通过RL模型与SL模型的基准对比验证了资源分配预测的准确性及相应成功决策的有效性。此外，补充的穷举搜索空间研究表明，不同资源对每种VNF类型性能的影响存在差异，这印证了多目标优化、针对每种VNF类型的个性化分析，以及自适应在线性能学习（如iOn-Profiler的自主在线学习方法）的必要性。