Collaborative Research: OAC Core: Small: Anomaly Detection and Performance Optimization for End-to-End Data Transfers at Scale

协作研究：OAC 核心：小型：大规模端到端数据传输的异常检测和性能优化

基本信息

批准号：
2007789
负责人：
Engin Arslan
金额：
$ 27.5万
依托单位：
Board of Regents, NSHE, obo University of Nevada, Reno
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2024-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007789&HistoricalAwards=false
关键词：
Collaborative Research OAC Core Small

项目摘要

Despite continuous efforts and investments to upgrade the networking infrastructure of research and education institutions to meet the needs of large-scale science applications, the data transfers on these networks often perform very poorly. Understanding the underlying reasons for poor transfer performance is important yet challenging due to the sophisticated design of today's cyberinfrastructures. This project offers a set of novel models and algorithms to detect and mitigate performance issues of data transfers in research networks. The proposed suite of tools helps researchers and system administrators to pinpoint the root cause of performance problems of data transfers so that necessary actions can be taken swiftly to minimize their impact on ongoing transfers. The project will also integrate the research into all levels of education, including science projects with K-12 students, development of new curriculum modules for graduate- and undergraduate-level courses, and summer workshops specifically for minority groups.Understanding the true underlying reasons for poor transfer performance is key to mitigating them and delivering the promised transfer speeds. However, the involvement of multiple end systems, dynamically changing background traffic, and the complexity of today's networking infrastructures turns it into a complicated and time-consuming process. This project develops a novel anomaly-detection and performance-optimization framework for end-to-end data transfers at scale. The framework helps to predict, understand, diagnose, and optimize wide-area file transfers in today's extreme-scale cyberinfrastructures. To achieve this goal, it derives deep-neural-network-based predictive models that can relate transfer settings to throughput. These models are then used to estimate the optimal configuration for new transfers. The framework also gathers performance metrics for end-system and network resources periodically to keep track of system utilization. When a transfer anomaly is detected, the collected metrics are fed into anomaly-classification models to identify the root causes. Once the underlying reasons of performance problems are identified, the framework launches a real-time optimization process to reconfigure the transfer settings such that the impact of anomalies can be alleviated.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.

尽管不断努力和投资来升级研究和教育机构的网络基础设施，以满足大规模科学应用的需求，但这些网络上的数据传输往往表现很差。由于当今网络基础设施的设计复杂，了解传输性能不佳的根本原因非常重要，但也具有挑战性。该项目提供了一组新颖的模型和算法来检测和缓解研究网络中数据传输的性能问题。拟议的工具套件可帮助研究人员和系统管理员查明数据传输性能问题的根本原因，以便迅速采取必要的措施，最大限度地减少对正在进行的传输的影响。该项目还将把研究整合到各级教育中，包括 K-12 学生的科学项目、为研究生和本科生课程开发新课程模块，以及专门针对少数群体的夏季研讨会。传输性能差是缓解这些问题并提供所承诺的传输速度的关键。然而，多个终端系统的参与、动态变化的后台流量以及当今网络基础设施的复杂性使其成为一个复杂且耗时的过程。该项目开发了一种新颖的异常检测和性能优化框架，用于大规模端到端数据传输。该框架有助于预测、理解、诊断和优化当今超大规模网络基础设施中的广域文件传输。为了实现这一目标，它派生了基于深度神经网络的预测模型，可以将传输设置与吞吐量联系起来。然后使用这些模型来估计新传输的最佳配置。该框架还定期收集终端系统和网络资源的性能指标，以跟踪系统利用率。当检测到传输异常时，收集的指标将被输入异常分类模型以识别根本原因。一旦确定了性能问题的根本原因，该框架就会启动实时优化流程来重新配置传输设置，从而减轻异常的影响。该奖项反映了 NSF 的法定使命，并通过使用评估结果被认为值得支持。基金会的智力价值和更广泛的影响审查标准。