I-Corps: SDNatics: Big Data Analytics of Software Defined Networks to Understand, Predict and Protect Critical Computer Networks

I-Corps：SDNatics：软件定义网络的大数据分析，用于理解、预测和保护关键计算机网络

• 批准号: 1530989
• 负责人: Yan Luo
• 金额: $ 5万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1530989&HistoricalAwards=false
• 关键词: Corps; SDNatics; Big; Data; Analytics

Conventional computer network management has serious limitations on granularity, responsiveness and predictability, which can lead to network failures costing millions of dollars in critical domains, e.g., public safety and health care. Today's networks are currently managed under various enterprise policies and with state-of-the-art tools such as software defined networking (SDN) controllers. However, the faults and problems in the networks still arise because there are always cases to which predefined rules and algorithms do not respond well, causing traffic flooding, congestions, or vulnerability to attacks. Understanding in-depth of the network behavior patterns and applying the knowledge in network management and planning are fundamental to providing new solutions and best practices.This team has developed a suite of protocols and analytics methods to understand network dynamics by using software-defined networking (SDN) and machine learning technologies. With this information the team is able to report abnormal network behavior and alert potential faults. This allows for much more of the network to be managed algorithmically, automatically, and intelligently. Specifically, this team's technology is able to track network flows to a much higher granularity (i.e. resolution) using SDN technology. However, while current SDN approaches are better than traditional networks in defining and enforcing rules around network flows, they do not have a capability to analyze in-depth usage behavior and dynamically apply such information to improve network management and planning. By using machine learning techniques (i.e. Deep Learning), the team will develop the SDNatics software platform that will allow for more advance uses of SDN. Such improvements should improve security, predictability, speed, and data volume in network systems.

传统的计算机网络管理在粒度、响应能力和可预测性方面存在严重限制，这可能导致公共安全和医疗保健等关键领域的网络故障造成数百万美元的损失。当今的网络目前根据各种企业策略并使用软件定义网络 (SDN) 控制器等最先进的工具进行管理。然而，网络中的故障和问题仍然会出现，因为预定义的规则和算法总是无法很好地响应，从而导致流量泛滥、拥塞或容易受到攻击。深入了解网络行为模式并应用网络管理和规划知识是提供新解决方案和最佳实践的基础。该团队开发了一套协议和分析方法，通过使用软件定义网络来了解网络动态（ SDN）和机器学习技术。 有了这些信息，团队就能够报告异常网络行为并警告潜在故障。 这使得更多的网络可以通过算法、自动和智能的方式进行管理。具体来说，该团队的技术能够使用 SDN 技术以更高的粒度（即分辨率）跟踪网络流。 然而，虽然当前的SDN方法在定义和执行网络流规则方面比传统网络更好，但它们不具备深入分析使用行为并动态应用此类信息来改进网络管理和规划的能力。 通过使用机器学习技术（即深度学习），该团队将开发 SDNatics 软件平台，该平台将允许更高级地使用 SDN。 此类改进应提高网络系统的安全性、可预测性、速度和数据量。