CC* Integration-Small: Network cyberinfrastructure innovation with an intelligent real-time traffic analysis framework and application-aware networking

CC* Integration-Small：网络基础设施创新，具有智能实时流量分析框架和应用感知网络

• 批准号: 2322369
• 负责人: Byravamurthy Ramamurthy
• 金额: $ 50万
• 依托单位: University of Nebraska-Lincoln
• 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
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322369&HistoricalAwards=false
• 关键词: CC; Integration; Small; Network; cyberinfrastructure; CC; Integration; Small; Network; cyberinfrastructure

Intelligent analytics approaches leveraging machine learning techniques offer new capabilities to analyze, model, predict and optimize traffic for high-throughput distributed computing workflows. These techniques can be greatly enhanced by access to real-world data from the edge (campus networks) and the core (Internet2) as well as Just-In-Time (JIT) machine learning approaches. Such a design allows for run-time deployment of the models at the campus cyberinfrastructure to make real-time network decisions. Network flow data collected from these cyberinfrastructures for analysis quickly scales up in size, making it infeasible to perform analysis of network flows in a realistic and timely manner. There are intrinsic difficulties stemming from data storage, its formatting and types as well as the manner in which traditional analysis is done to study network flow data. Although advances have been made in the past several years in how data could be handled efficiently, the new techniques have not been integrated well into the network operations. Improvements need to be made in the way network flow data is analyzed by exploiting the modern data storage formats and the intrinsic properties of the network flow data as well as by developing efficient data structures and algorithms. Recent advances in networking allow for fine-grained network control policies to be managed by network applications. Although it is possible to improve the overall performance of scientific data transfers end-to-end, problems exist with managing resources and differentiating network services at the experiment/site level. Designing and developing intelligent network analysis by JIT machine learning paradigms strengthened by a scalable network flow analysis framework for an application-aware control of the network in high-throughput computing frameworks is the goal of this project. The project is strengthened by collaborations with Holland Computing Center (HCC) at UNL, Open Science Consortium (OSG), Argonne National Lab (ANL) and Internet2. The techniques and frameworks developed in this project will be made available to the open-source community, thus benefiting other science application use cases in Research and Education (R&E) networks. Enriching the education opportunities for UNL School of Computing students and conducting outreach events for the broader community are important objectives of this project.The project aims to transform the current cyberinfrastructure networking approach by (1) gaining insights in real-time by the development and integration of online-offline approaches to machine learning (unlike traditional offline approaches) that can be deployed in data centers for real-time network traffic analysis and prediction; (2) scalable analysis of network flow data by implementing the developed theoretical models for transforming, indexing and building search techniques to study the network flow data at internet-scale in real-time and (3) application-aware control of data transfers by application-aware software defined networking (SDN) control strategies to provide greater flexibility in network management and service differentiation for scientific data transfers on campus cyberinfrastructures.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.

智能分析方法利用机器学习技术为分析，建模，预测和优化高通量分布式计算工作流程的流量提供了新的功能。通过从Edge（校园网络）和Core（Internet2）以及Just-Undime（JIT）机器学习方法访问现实世界数据，可以大大增强这些技术。这样的设计允许在校园网络基础结构中运行模型的运行时间，以做出实时网络决策。从这些网络基础设施收集的网络流数据以迅速扩大规模，因此以现实和及时的方式对网络流进行分析是不可行的。数据存储，其格式和类型以及进行传统分析来研究网络流数据的方式存在内在的困难。尽管在过去几年中，在如何有效地处理数据方面已经取得了进步，但新技术并未很好地集成到网络运营中。需要通过利用现代数据存储格式以及网络流数据的内在属性以及开发有效的数据结构和算法来分析网络流数据的方式进行改进。网络的最新进展允许通过网络应用程序管理细粒网络控制策略。尽管有可能提高科学数据传输端到端的总体绩效，但在管理资源和在实验/站点层面上区分网络服务的问题存在问题。通过可扩展的网络流量分析框架来设计和开发通过JIT机器学习范式来设计和开发智能网络分析，以在高通量计算框架中对网络进行应用程序感受的控制是该项目的目标。通过与荷兰计算中心（HCC）合作，开放科学联盟（OSG），Argonne National Lab（ANL）和Internet2的合作，该项目得到了加强。该项目中开发的技术和框架将提供给开源社区，从而使研究和教育（R＆E）网络中的其他科学应用程序案例受益。在（1）通过（1）通过（1）通过（1）通过（1）实时获得实时的洞察力来改变当前的网络基础结构网络方法，通过（1）通过开发和集成在线学习方法的机器学习方法（与传统的离线方法）在数据中心分析，并为实体分析而实现，该项目是通过（1）实时获得实时的洞察力，并将其实时地分析; （2）通过实施开发的理论模型来转换，索引和构建搜索技术，以实时研究网络流量数据，以及（3）通过应用程序了解软件定义的网络（SDN）控制策略，以提供更大的灵活性，以对网络管理和服务差异的灵活性，可用于网络流量数据传输，以实现网络流量数据的可扩展分析。法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。