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

网络功能虚拟化环境中基于计算智能的Evolve Packet核心功能实现分析

基本信息

  • 批准号:
    485301-2015
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Engage Grants Program
  • 财政年份:
    2015
  • 资助国家:
    加拿大
  • 起止时间:
    2015-01-01 至 2016-12-31
  • 项目状态:
    已结题

项目摘要

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.
从2012年开始,网络功能虚拟化的概念也引起了人们的极大兴趣 作为重要性。作为允许网络功能虚拟化的网络架构概念, 网络功能虚拟化允许有效地实现专门的网络服务。 换句话说,网络功能虚拟化允许在标准上实现网络功能。 无需专有硬件的计算平台。 随着移动的设备的增长和移动的网络的利用率的提高,管理 网络功能虚拟化的基本组件,即,虚拟网络功能具有特殊的 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 正确启动、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 用于分析表示用于实现的现成硬件组件的利用率的数据的方法 专用演进分组核心(用于通过移动的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 以便预见和识别性能下降、潜在故障和自我修复自动化。的 该项目是与工业合作伙伴Expeto Wireless Inc.一起进行的。Expeto提供专业知识, 以及在各种情况下实现虚拟网络功能时收集的数据。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Reformat, Marek其他文献

Multilabel associative classification categorization of MEDLINE articles into MeSH keywords - An intelligent data mining technique to more accurately classify large volumes of documents
Automatic test data generation using genetic algorithm and program dependence graphs
  • DOI:
    10.1016/j.infsof.2005.06.006
  • 发表时间:
    2006-07-01
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Miller, James;Reformat, Marek;Zhang, Howard
  • 通讯作者:
    Zhang, Howard
Human intelligence-based metaverse for co-learning of students and smart machines.
Wind power forecasting using attention-based gated recurrent unit network
  • DOI:
    10.1016/j.energy.2020.117081
  • 发表时间:
    2020-04-01
  • 期刊:
  • 影响因子:
    9
  • 作者:
    Niu, Zhewen;Yu, Zeyuan;Reformat, Marek
  • 通讯作者:
    Reformat, Marek
xGENIA: A comprehensive OWL ontology based on the GENIA corpus.
XGenia:基于Genia语料库的综合猫头鹰本体。
  • DOI:
    10.6026/97320630001360
  • 发表时间:
    2007-03-20
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Rak, Rafal;Kurgan, Lukasz;Reformat, Marek
  • 通讯作者:
    Reformat, Marek

Reformat, Marek的其他文献

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{{ truncateString('Reformat, Marek', 18)}}的其他基金

Knowledge Extraction via Learning Processes and Data Models with Imprecision
通过不精确的学习过程和数据模型提取知识
  • 批准号:
    RGPIN-2017-06245
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Knowledge Extraction via Learning Processes and Data Models with Imprecision
通过不精确的学习过程和数据模型提取知识
  • 批准号:
    RGPIN-2017-06245
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Data-driven system for predicting outages and their severity
用于预测中断及其严重程度的数据驱动系统
  • 批准号:
    537808-2018
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Data-driven system for predicting outages and their severity
用于预测中断及其严重程度的数据驱动系统
  • 批准号:
    537808-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Knowledge Extraction via Learning Processes and Data Models with Imprecision
通过不精确的学习过程和数据模型提取知识
  • 批准号:
    RGPIN-2017-06245
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Data-driven modeling of refinery reactors
炼油反应器的数据驱动建模
  • 批准号:
    533718-2018
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Grants Program
Knowledge Extraction via Learning Processes and Data Models with Imprecision
通过不精确的学习过程和数据模型提取知识
  • 批准号:
    RGPIN-2017-06245
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Knowledge Extraction via Learning Processes and Data Models with Imprecision
通过不精确的学习过程和数据模型提取知识
  • 批准号:
    RGPIN-2017-06245
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Computational intelligence based analysis of power distribution data
基于计算智能的配电数据分析
  • 批准号:
    514064-2017
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Grants Program
Data-driven vehicle health management framework
数据驱动的车辆健康管理框架
  • 批准号:
    490536-2015
  • 财政年份:
    2016
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants

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