NeTS: Small: Efficient Techniques for Failure Recovery and Tomography in Networks

NeTS：小型：网络中故障恢复和层析成像的高效技术

• 批准号: 1117274
• 负责人: Loukas Lazos
• 金额: $ 30万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117274&HistoricalAwards=false
• 关键词: NeTS; Small; Efficient; Techniques; Failure

The emergence of rich interactive collaborative applications on desktop and mobile platforms demands a robust network infrastructure. The network must recover quickly from link and node failures and must do so while having only a small amount of information. This project seeks to develop scalable proactive failure recovery techniques that require only a constant factor increase in the information maintained at every node. The techniques developed in this project are applicable to circuit-switched and packet-switched networks. In order to improve the robustness of the network further, link and node performances may be monitored periodically by observing the behavior of end-to-end paths, referred to as network tomography. This project seeks to develop fundamental theories and practical algorithms for the identifiability of link and node metrics from end-to-end path observations. The expected results from this project include: (1) algorithms and protocols that may be employed in the data link and network layers of the protocols stack; (2) tools for evaluating different recovery and monitoring techniques; and (3) novel games based on the research problems, targeted at mobile platforms such as iPhone/iPod touch and Android-based devices. The results of the project will be integrated into the undergraduate and graduate networking and graph theory courses.The intellectual merit of the project is in identifying the fundamental graph theoretical problems underlying the various techniques developed for recovering from multiple link failures and network tomography. The result will lead to new insights into network design, operation, and monitoring with guaranteed performance.The results of this project will have significant broader impact. The algorithms developed for network tomography is applicable to similar problems in the fields of nanoelectronics, compressive sensing, and power systems management. The impact of games derived from the research concepts will reach beyond the intended audience, as the games will be made available through the iTunes App Store for world-wide dissemination and students will share the games their siblings, relatives, and friends.

桌面和移动平台上丰富的交互式协作应用程序的出现需要强大的网络基础设施。网络必须从链路和节点故障中快速恢复，并且必须在只有少量信息的情况下这样做。该项目旨在开发可扩展的主动故障恢复技术，该技术只需要在每个节点上维护恒定的信息。本课题开发的技术适用于电路交换和分组交换网络。为了进一步提高网络的鲁棒性，可以通过观察端到端路径的行为来周期性地监视链路和节点的性能，这被称为网络断层扫描。该项目旨在开发基于端到端路径观测的链路和节点度量的可识别性的基本理论和实用算法。本项目的预期成果包括：(1)协议栈的数据链路层和网络层可能采用的算法和协议；(2)评估不同恢复和监测技术的工具；(3)针对iPhone/iPod touch和android等移动平台的新游戏。该项目的成果将被整合到本科和研究生的网络和图论课程中。该项目的智力价值在于确定了用于从多链路故障和网络断层扫描中恢复的各种技术的基本图理论问题。研究结果将为保证性能的网络设计、操作和监控带来新的见解。这个项目的成果将产生重大而广泛的影响。为网络层析成像开发的算法适用于纳米电子学、压缩感知和电力系统管理等领域的类似问题。来自研究概念的游戏的影响将超越目标受众，因为游戏将通过iTunes App Store进行全球传播，学生将与兄弟姐妹，亲戚和朋友分享游戏。