NeTS:Small:Content Retrieval Networks: Network Access for Disadvantaged Regions

NeTS:Small:内容检索网络：弱势地区的网络接入

• 批准号: 0916204
• 负责人: Vivek Pai
• 金额: $ 50万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916204&HistoricalAwards=false
• 关键词: NeTS; Small; Content; Retrieval; Networks

This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5).Even in the US, many regions suffer poor Internet connectivity, due tohigh prices, capacity limitations, poor infrastructure, or lowpopulation density. Rural communities may be underserved because ofDSL distance limits and the high cost of bringing fiber optic cable tolow-density regions. Even when businesses or schools can be connectedby DSL, its capacity limits may still provide poor end-user experiencewhen many people share the connection. This research is addressing theseproblems by using intelligent software to enhance networkconnectivity. By aggressive data caching and off-peak prefetching,data can be stored on disk and retrieved locally instead of alwaysbeing fetched from the Internet. For environments with many users,this system can reduce bandwidth consumption by an order of magnitude,providing higher effective end-user bandwidth, or allow costreductions by purchasing less wide-area bandwidth. The expectedoutcome of this research is an open-source system that can be deployedat Internet gateways, which will transparently improve networkperformance. Possible locations include schools, small businesses, andInternet Service Providers (ISPs). In schools, when a classroom fullof students accesses a topic, only one copy is downloaded, andthe rest get it from the cache. For news sites that update during theday, only the changed portions get downloaded, instead of wholearticles. Prefetching will also reduce latency, by determining userpatterns and preloading content before it is needed.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。即使在美国，由于价格高、容量限制、基础设施落后或人口密度低，许多地区的互联网连通性也很差。由于dsl距离的限制和在低密度地区铺设光缆的高成本，农村社区可能服务不足。即使企业或学校可以通过DSL连接，当许多人共享连接时，其容量限制可能仍然会提供糟糕的最终用户体验。这项研究通过使用智能软件来增强网络连接来解决这些问题。通过积极的数据缓存和非峰值预取，数据可以存储在磁盘上并在本地检索，而不是总是从互联网获取。对于具有许多用户的环境，该系统可以将带宽消耗降低一个数量级，从而提供更高的有效终端用户带宽，或者通过购买更少的广域带宽来降低成本。这项研究的预期结果是一个可以部署在互联网网关上的开源系统，这将明显提高网络性能。可能的地点包括学校、小型企业和互联网服务提供商（isp）。在学校，当满教室的学生访问一个主题时，只下载一个副本，其余的都从缓存中获取。对于白天更新的新闻网站，只下载更改的部分，而不是整篇文章。通过确定用户模式并在需要之前预加载内容，预取还可以减少延迟。