IRNC: AMI: Collaborative Research: Software-Defined and Privacy-Preserving Network Measurement Instrument and Services for Understanding Data-Driven Science Discovery
IRNC: AMI: Collaborative Research: Software-Defined and Privacy-Preserving Network Measurement Instrument and Services for Understanding Data-Driven Science Discovery
批准号:
1450996
负责人:
Yan Luo
金额:
$125.49万
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
项目状态:
已结题
起止时间:
2015-04-01 至 2020-03-31
中文摘要
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英文摘要
Data intensive science discovery at a global scale has imposed new requirements on the speed and management of international research and education networks. At the connection points of these international networks, it is critical to measure the network data flows to understand network traffic patterns, identify network anomalies and provide insights to network control and planning. However, the ever-increasing network speed, the massive amount of network flows and the changing measurement objectives have made the flow-level measurement on very high-speed networks extremely challenging. The Advanced Measurement Instrument and Services (AMIS) project leverages many-core, programmable network processors to prototype and deploy an advanced measurement instrument to enable services for accurate network monitoring and in-depth traffic analysis. The instrument supports flow-granularity measurement at line rate up to 100Gbps and software application programming interfaces to examine selected flows, with no impact to the performance of user traffic. With scalable hardware and an open source software stack, the measurement services equip network operators with effective tools to quantify flow-level network performance and study network flows through privacy-preserving computational analytics. This project is built on a consortium of academia, industrial partners, network operators and international alliances, who bring unique expertise and resources to achieve the objectives of high performance, programmable flow-granularity network measurement. The outcomes from this project will significantly benefit data driven science discovery, such as astronomy and space weather studies, and will promote broadened participation of underrepresented groups (such as Hispanic and female students) through the involvement of multiple universities, including an EPSCoR university and a Hispanic Serving Institution.
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