Collaborative Research: CNS Core: Small: Accelerating Serverless Cloud Network Performance
Collaborative Research: CNS Core: Small: Accelerating Serverless Cloud Network Performance
批准号:
2229455
负责人:
Vinod Yegneswaran
金额:
$29.98万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-01-01 至 2025-12-31
中文摘要
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英文摘要
Serverless computing has revolutionized cloud programming and is poised to become the next dominant cloud computing paradigm. Among the major allures of serverless computing is “agile autoscaling”, which brings forth both efficiency and economic advantages. However, such flexibility is accompanied by new network performance challenges that are uniquely endemic to the serverless compute environments. For example: (1) internal function chain communication becomes a bottleneck for serverless applications and (2) a unified function gateway increases the number of indirect connections in function chains that further impairs network performance. This project proposes a cross-layered effort that seeks to optimize the function chain communication in severless-cloud environments by exploring multiple, synergistic strategies to reduce latency in function-chain communication. The proposed solution includes (1) a new QUIC-based network substrate that can simultaneously improve performance and security, without the need for tenant code modification and (2) use of data-plane programming and informed request prediction to optimize resource allocation and mitigate cold-start issues in serverless multi-tenant clouds. This work will empower a broad class of novel serverless applications with stringent latency and security requirements and our proposed designs will be universally applicable across popular cloud platforms. This project will make the proposed implementations open-source and publicly release our evaluation environments through the NSF Fabric testbed, thus benefiting diverse stakeholders. This project will actively explore the transition of our work to commercial cloud services and open-source serverless communities, such as OpenFaaS and the QUIC-go project. This project will incorporate a detailed education and outreach plan targeting undergraduate and graduate researchers (through courses and summer internships), and women and minorities (through organizations like ThriveWiSE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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TWC: TTP Option: Medium: Collaborative: MALDIVES: Developing a Comprehensive Understanding of Malware Delivery Mechanisms
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批准号:1514503
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项目类别:Standard Grant
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资助金额:$64.98万
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财政年份:2015
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负责人:Vinod Yegneswaran
-
依托单位:
TWC: Medium: Collaborative: HIMALAYAS: Hierarchical Machine Learning Stack for Fine-Grained Analysis of Malware Domain Groups
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批准号:1314956
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项目类别:Standard Grant
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资助金额:$59.61万
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财政年份:2013
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负责人:Vinod Yegneswaran
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依托单位:
国内基金
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