CAREER: Argus: A Measurement-informed Learning Approach to Managing Multi-cloud Networks

职业：Argus：管理多云网络的基于测量的学习方法

• 批准号: 2145813
• 负责人: Ramakrishnan Durairajan
• 金额: $ 52.91万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145813&HistoricalAwards=false
• 关键词: CAREER; Argus; Measurement; informed; Learning

Multi-cloud networks are federations of private network infrastructures from the distinct cloud and third-party providers, and serve as increasingly vital underlays for a range of application domains such as genomics, healthcare, and high performance computing. This emerging connectivity paradigm poses significant management barriers to enterprises that seek to deploy overlays and applications due to providers' distinct operational practices, privacy concerns, egress costs, among others. This CAREER project will investigate a novel measurement-informed learning-based framework called Argus to significantly lower the management barriers faced by modern enterprises.This project will focus on scientific inquiries in three synergistic thrusts to realize the Argus framework. First, it will design calibrated measurement tools and techniques, using which enterprises can gain visibility into the federated underlays. Second, adhering to the privacy concerns of providers, it will investigate learning-based modeling capabilities, using which enterprises can accurately infer, localize, and attribute performance bottlenecks to appropriate providers. Third, it will take a principled approach to design a management capability, using which enterprises can effectively and efficiently navigate egress costs and operational goals while avoiding inferred performance bottlenecks.This project's goal is to lower multi-cloud management barriers, to enhance the operational productivity of enterprises, and to foster breakthroughs in the aforementioned domains and beyond. The research will be tightly integrated with education, emphasizing experiential learning. Activities include inviting underrepresented students from local community colleges to participate in a mini-research experience, organizing virtual summer schools on project-related topics, involving undergraduates in research, and developing a curriculum on multi-cloud networks. Software artifacts, publications, and course materials resulting from this project will be made available at https://ix.cs.uoregon.edu/~ram/CAREER.html. The website will be actively maintained during the entire period of the project, and for at least one year past the ending of the project.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.

多云网络是来自不同云和第三方提供商的私有网络基础设施的联合，并作为一系列应用领域（如基因组学，医疗保健和高性能计算）的日益重要的基础。由于提供商不同的运营实践、隐私问题、出口成本等原因，这种新兴的连接模式对寻求部署覆盖层和应用程序的企业构成了重大的管理障碍。本CAREER项目将研究一种新的基于测量的学习框架，称为Argus，以显著降低现代企业面临的管理障碍。本项目将重点关注实现Argus框架的三个协同目标的科学探究。首先，它将设计校准的测量工具和技术，使用这些工具和技术，企业可以获得对联合基础的可见性。其次，坚持对供应商的隐私问题，它将调查基于学习的建模能力，使用它企业可以准确地推断，本地化和属性的性能瓶颈，以适当的供应商。第三，以原则性的方式设计管理能力，使企业能够有效、高效地驾驭出口成本和运营目标，同时避免推断出的性能瓶颈。本项目的目标是降低多云管理障碍，提高企业的运营生产力，促进上述领域及其他领域的突破。研究将与教育紧密结合，强调体验式学习。活动包括邀请当地社区学院代表性不足的学生参加小型研究体验，组织与项目相关主题的虚拟暑期学校，让本科生参与研究，以及开发多云网络课程。本项目产生的软件工件、出版物和课程材料将在https://ix.cs.uoregon.edu/~ram/CAREER.html上提供。该网站将在整个项目期间积极维护，并在项目结束后至少一年。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

ARISE: A Multitask Weak Supervision Framework for Network Measurements

• DOI: 10.1109/jsac.2022.3180783
• 发表时间: 2022-08
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: Jared Knofczynski;Ramakrishnan Durairajan;W. Willinger