With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) placement problem by developing a machine learning decision tree model that learns from the effective placement of the various VNF instances forming a Service Function Chain (SFC). The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances. The benefits of using machine learning are realized by moving away from a complex mathematical modelling of the system and towards a data-based understanding of the system. Using the Evolved Packet Core (EPC) as a use case, we evaluate our model on different data center networks and compare it to the BACON algorithm in terms of the delay between interconnected components and the total delay across the SFC. Furthermore, a time complexity analysis is performed to show the effectiveness of the model in NFV applications.
翻译:由于对数据连通性的需求不断增加,网络服务供应商面临减少资本和业务开支的任务,同时改善网络绩效和满足更大的连通需求。虽然网络功能虚拟化(NFV)已被确定为一种解决办法,但为确保可行性,必须应对若干挑战。在本文件中,我们通过开发一个机器学习决策树模型来解决虚拟网络功能(VNF)定位问题,该模型从形成服务功能链的各种VNF实例的有效定位中吸取经验教训。该模型从网络中将若干与业绩有关的功能作为输入,并选择将各种VNF实例放置在网络服务器上,以尽量减少依赖VNF实例之间的延迟。使用机器学习的好处是通过摆脱系统的复杂的数学模型和向基于数据的理解系统前进来实现的。我们用Evolved Packet Core(ECPC)作为使用实例,对不同数据中心网络的模型进行了评估,并将它与BACON算法的计算方法进行比较,因为各部分之间相互联系,整个SFCFCF的延迟。此外,还进行了时间复杂性分析,以显示NFV应用模型的有效性。