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专用网络基础架构的联合会，并且在一系列应用领域（例如基因组学，医疗保健和高性能计算）中，越来越重要。这种新兴的连通性范式为企业构成了重大的管理障碍，这些企业试图部署由于提供者独特的运营实践，隐私问题，出口成本等，以部署叠加和应用程序。该职业项目将调查一个名为Argus的新型基于学习的基于学习的框架，以大大降低现代企业所面临的管理障碍。该项目将重点关注三个协同推力的科学询问，以实现Argus框架。首先，它将设计校准的测量工具和技术，使用企业可以在这些工具上获得联合层层的可见性。其次，遵循提供商的隐私问题，它将研究基于学习的建模功能，使用企业可以准确地推断，本地化和归因于适当的提供商的性能瓶颈。第三，它将采用一种原则性的方法来设计管理能力，使用企业可以有效，有效地导航出口成本和操作目标，同时避免推断出的绩效瓶颈。该项目的目标是降低多云管理障碍，以增强企业的运营生产力，以增强企业的运营生产力，并在本土范围内促进分裂的影响。该研究将与教育紧密融合，强调体验式学习。活动包括邀请当地社区大学的代表性不足的学生参加小型研究经验，组织与项目有关的主题的虚拟暑期学校，涉及研究本科生，并在多云网络上开发课程。该项目产生的软件文物，出版物和课程材料将在https://ix.cs.uoregon.edu/~ram/career.html上提供。该网站将在项目的整个期间内积极维护，并在项目结束后至少一年内进行。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的审查标准，被认为值得通过评估来获得支持。

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