自适应路由服务合成:模型及优化

Adaptive Routing Service Composition:Modeling and Optimization

当前,新型网络应用不断涌现,用户对不同类型应用的通信需求也呈现出多样化和个性化的特点.面向用户频繁产生和变化的通信需求,网络服务提供商(Internet service provider,简称ISP)通常以不断地购买及部署大量新型的专用网络设备的方式来应对,导致其运营成本高昂,资源浪费严重,网络建设与发展的可持续性差.对此,从软件角度出发,考虑路由功能重用,通过选择合适的路由功能,在通信路径上为应用合成定制化的路由服务,满足用户差异化的需求.基于网络功能虚拟化(network function virtualization,简称NFV)和软件定义网络(software-defined networking,简称SDN),提出了一种自适应路由服务合成机制,运用软件产品线技术构建路由服务产品线,作为路由功能选择和路由服务优化的基础.基于机器学习,运用多层前馈神经网构建路由服务离线模式和在线模式两阶段学习模型,对路由功能选择及组合进行持续学习和优化,实现路由服务的定制化目标,以提高用户的服务体验.进行了仿真实现,研究结果表明,所提出的模型是可行和有效的.

Currently, a lot of new types of applications are constantly emerging, and the user communication demands for different applications are also becoming diversified and personalized. To match users＇ frequent and changing communication demands, interact service provider （ISP） usually constantly purchases and operates new specialized network equipment, which leads to high operating cost and resource waste, and it is obviously unsustainable for network construction and development. This paper addresses the above challenge from the perspective of software-based method by reusing diverse routing functions. The suitable routing functions are selected to compose the customized routing services on communication paths of applications, in order to satisfy the user demands. Based on network function virtualization （NFV） and software defined networking （SDN）, the paper proposes an adaptive routing service composition mechanism. It leverage software product line （SPL） to establish routing service product line, which serves as the basis to select routing functions and optimize routing services. In addition, based on machine learning, it establishes two-phased routing service learning model, that is, offiine mode and online mode, by leveraging multilayer feed-forward neural network. It can constantly adjust and optimize routing function selection and service composition to achieve routing service customization and improve user service experience. Simulation and performance results show that the proposed model is feasible and efficient.