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Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management

合作研究：CNS：媒介：无线网络管理的分布式数据可扩展学习

基本信息

批准号：
2106946
负责人：
Zaoxing Liu
金额：
$ 20万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106946&HistoricalAwards=false
关键词：
Collaborative Research CNS Medium Scalable

项目摘要

The transition to 5G is expected to witness not only an emergence of new applications such as mobile augmented and virtual reality, but also opens up the attack surface to both known, and previously unknown threats. Thus, wireless networks of the future will need better control and management at different temporal and traffic aggregation granularities (e.g., how to allocate spectrum, how to quarantine distributed attacks etc.). This project aims to develop scalable, machine learning based analytics on the data from a large set of geographically distributed wireless core network entities such as base stations. The research will enable new approaches for: (a) compressing the raw data via novel summaries and sketches, that reduce overhead while simultaneously enabling highly accurate scalable analytics (b) scalable yet highly flexible distributed learning approaches that are built upon the emerging federated learning paradigm and (c) flexible allocation of bandwidth to support the control plane analytics that minimizes the impact on the data plane. The proposed research outcomes will be systems, algorithms, and data analytics workflows that will inform the design and management of next generation critical wireless infrastructures. The approaches developed will enable ISPs to better apportion resources and enable better performance for emerging augmented reality applications for societal benefit (e.g., disaster response and management). In addition, the approaches can enable the discovery and profiling of new threats, which will have significant implications on national security. The proposed education activities are expected to provide students with a comprehensive training in networking, security, system building, and data science. Thus, there is significant potential for broader impact in terms of contributions to workforce development in an area of national need.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.

预计向5G的过渡不仅会见证移动的增强现实和虚拟现实等新应用的出现，还会为已知和以前未知的威胁打开攻击面。因此，未来的无线网络将需要在不同的时间和业务聚合粒度（例如，如何分配频谱、如何隔离分布式攻击等）。该项目旨在开发基于机器学习的可扩展分析，分析来自大量地理分布的无线核心网络实体（如基站）的数据。该研究将实现新的方法：（a）通过新颖的摘要和草图压缩原始数据，减少开销，同时实现高度准确的可扩展分析（B）可扩展但高度灵活的分布式学习方法，建立在新兴的联邦学习范式基础上，（c）灵活分配带宽，以支持控制平面分析，最大限度地减少对数据平面的影响。拟议的研究成果将是系统，算法和数据分析工作流程，这些工作流程将为下一代关键无线基础设施的设计和管理提供信息。所开发的方法将使互联网服务提供商能够更好地分配资源，并为新兴的增强现实应用提供更好的性能，以造福社会（例如，灾害应对和管理）。此外，这些方法可以发现和分析新的威胁，这将对国家安全产生重大影响。拟议的教育活动预计将为学生提供网络，安全，系统构建和数据科学方面的全面培训。因此，在国家需要的领域对劳动力发展的贡献方面具有更广泛的影响力的巨大潜力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

In-Memory Key-Value Store Live Migration with NetMigrate

使用 NetMigrate 进行内存中键值存储实时迁移

DOI：
发表时间：
2024
期刊：
22nd USENIX Conference on File and Storage Technologies (FAST '24
影响因子：
0
作者：
Zhu, Zeying;Zhu, Yibo;Liu, Zaoxing
通讯作者：
Liu, Zaoxing

Precise error estimation for sketch-based flow measurement

DOI：
10.1145/3487552.3487856
发表时间：
2021-11
期刊：
Proceedings of the 21st ACM Internet Measurement Conference
影响因子：
0
作者：
Peiqing Chen;Yuhan Wu;Tong Yang;Junchen Jiang;Zaoxing Liu
通讯作者：
Peiqing Chen;Yuhan Wu;Tong Yang;Junchen Jiang;Zaoxing Liu

Flow-level loss detection with Δ-sketches

DOI：
10.1145/3563647.3563653
发表时间：
2022-10
期刊：
Proceedings of the Symposium on SDN Research
影响因子：
0
作者：
Shir Landau Feibish;Zaoxing Liu;Nikita Ivkin;Xiaoqi Chen;V. Braverman;J. Rexford
通讯作者：
Shir Landau Feibish;Zaoxing Liu;Nikita Ivkin;Xiaoqi Chen;V. Braverman;J. Rexford

SketchLib: Enabling Efficient Sketch-based Monitoring on Programmable Switches

SketchLib：在可编程交换机上实现基于草图的高效监控

DOI：
发表时间：
2022
期刊：
19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22
影响因子：
0
作者：
Namkung, Hun;Liu, Zaoxing;Kim, Daehyeok;Sekar, Vyas;Steenkiste, Peter
通讯作者：
Steenkiste, Peter

Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams

在多维数据流中实现高效、通用的子群体分析