iOn-Profiler：基于强化学习的智能在线多目标VNF性能剖析
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning
AI总结：
针对现有VNF资源性能剖析仅支持单目标优化的局限，提出基于强化学习的iOn-Profiler多目标在线剖析器，可在满足KPI的同时最小化多资源消耗并优化输出速率，实验验证其性能优于监督学习模型。
摘要：
要发挥虚拟化网络功能（Virtualised Network Functions, VNFs）的潜力，需清晰理解资源消耗与性能之间的关联。现有最先进方法针对给定网络环境和VNF类型，利用机器学习（Machine Learning, ML）尤其是监督学习（Supervised Learning, SL）模型实现这一目标，但均假设单目标优化场景。本研究采用不同思路，提出一种新型VNF剖析器，通过适配后的强化学习（Reinforcement Learning, RL）优化多资源类型分配与性能目标。与现有单目标方案相比，我们的方法既能满足关键性能指标（Key Performance Indicator, KPI）要求，又能最小化多资源类型消耗并优化VNF输出速率。我们针对3种真实VNF类型、共39个研究场景（每个VNF对应13个场景）开展实验评估，涉及3种资源类型（虚拟CPU、内存和网络链路容量），通过将我们的RL模型与SL模型进行基准对比，验证了资源分配预测的准确性以及相应剖析决策的有效性。我们还开展了补充性的穷尽搜索空间研究，结果表明不同资源对不同类型VNF的性能影响程度存在差异，这意味着多目标优化、针对每种VNF类型的个性化分析，以及自适应在线剖析学习（如iOn-Profiler的自主在线学习方法）十分必要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
