Software Defined Cognitive Networking: Intelligent Resource Provisioning For Future Networks

越来越多的自适应媒体应用之间对网络资源的非合作竞争对用户体验和网络效率造成了严重的不利影响。这可能会对数字经济和数字公共服务产生连锁反应，它们越来越依赖高质量和可靠的媒体流。现有的网络基础设施通常优先考虑改进的网络覆盖和快速分组转发功能，这并不总是有效地改善用户体验。归根结底，用户体验质量和网络效率是在线媒体分发的两个最重要的基准。未来的网络管理必须充分利用应用和用户层面的认知因素，以便有效和智能地分配稀缺的网络资源。这是第一个GRANT项目，旨在开发软件定义的认知网络(SDCN)，以确保在线自适应媒体的用户体验、用户级别的公平性和网络效率，使用SDN辅助和QOE感知的资源管理。SDCN将为从传统的资源提供和流量工程方案到上下文感知网络管理的重大飞跃奠定基础。为了实现其目标，该项目将开发一个认知模型，该模型基于对自适应媒体体验的人的因素的分析、主观实验的迭代和数据建模。该模型将启用非侵入式的QOE评估服务，该服务可监控自适应媒体流，并使用多个应用、服务和网络级指标评估其感知用户体验。该模型将使用专门构建的多目标资源分配函数来推导出最优解决方案，以在一个网段中为改善的用户体验、公平性和网络效率提供可用的网络资源。

The non-cooperative competition of network resources between a growing number of adaptive media applications has a significant detrimental impact on user experience and network efficiency. This can lead to knock-on effects to the digital economy and digital public services, which are increasingly dependent on high quality and reliable media streaming. Existing network infrastructures often prioritise improved network coverage and fast packet forwarding functions, which do not always effectively contribute to the improved user experience. Ultimately, the quality of user experience and network efficiency are the two of the most important benchmarks for online media distribution. Future network management must leverage application and user-level cognitive factors in order to allocate scarce network resources effectively and intelligently. this First Grant project aims at developing software defined cognitive networking (SDCN) to ensure the user experience, user-level fairness and network efficiency of online adaptive media using SDN-assisted and QoE-aware resource management. SDCN will lay the groundwork for a great leap from the conventional resource provisioning and traffic engineering schemes to context-aware network management. In order to achieve its objective, the project will develop a cognitive model based on the analysis of human factors of adaptive media experience, iterations of subjective experiments, and data modelling. The model will enable a non-intrusive QoE assessment service that monitors adaptive media flows and estimates their perceptual user experience using a number of application, service, and network-level metrics. The model will use a purpose-built multi-objective resource allocation function to derive optimal solutions to provision available network resource for the improved user experience, fairness and network efficiency in a network segment.