Computational Intelligence based Analysis of Evolve Packet Core Function Realizations in Network Function Virtualization Environment

自2012年诞生以来，网络功能虚拟化的概念也引起了人们的极大兴趣 具有重要意义。被视为允许网络功能的虚拟化的网络体系结构概念， 网络功能虚拟化使专业化网络服务得以有效实现。 换句话说，网络功能虚拟化允许在标准的基础上实现网络功能 不需要专有硬件的计算平台。 随着移动设备的增长和移动网络利用率的提高，管理流程 网络功能虚拟化的基本组成部分，即虚拟网络功能具有特殊性 重要性。为了确保可靠和可扩展的端到端服务，虚拟网络功能必须 适当地启动、监控和控制。 拟议的项目旨在适应机器学习/数据挖掘和计算智能 用于分析表示现成硬件组件的利用率的数据的方法 专用演进分组核心(通过移动网络提供语音和数据的框架)功能 在网络功能虚拟化环境中。该项目将产生处理数据的方法 以预测和识别性能下降、潜在故障和自我修复自动化。这个 该项目是与工业合作伙伴Expeto Wireless Inc.共同进行的。Expeto提供专业知识，如 以及在各种情况下实现虚拟网络功能时所采集的数据。

From its onset in 2012, the notion of Network Function Virtualization is gaining a substantial interest, as well as significance. Seen as a network architecture concept allowing for virtualization of network functions, Network Function Virtualization permits an effective realization of specialized networking services. In other words, Network Function Virtualization allows for implementing network functions on standard computing platforms without a need for proprietary hardware. With the growth of the mobile devices and increased utilization of mobile networks, the processes of managing essential components of Network Function Virtualization, i.e., Virtual Network Functions are of special importance. In order to ensure reliable and scalable end-to-end services, Virtual Network Functions have to be properly initiated, monitored, and controlled. The proposed project aims at adapting Machine Learning/Data Mining and Computational Intelligence methods for analyzing data representing utilization of off-the-shelf hardware components for implementing specialized Evolved Packet Core (a framework for providing voice and data over a mobile network) functions in the Network Function Virtualization environment. The project will result in methodology for processing data in order to foresee and identify performance degradation, potential failures and self-healing automation. The project is conducted together with an industrial partner, Expeto Wireless Inc.. Expeto provides expertise, as well as data collected during realization of Virtual Network Functions in various conditions.