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

尽管不断努力和投资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得非常糟糕。由于当今网络基础设施的复杂设计，了解传输绩效不佳的根本原因是重要的，但也是具有挑战性的。该项目提供了一套新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，以便能够迅速采取必要行动，将其对正在进行的传输的影响降至最低。该项目还将把这项研究整合到所有级别的教育中，包括针对K-12学生的科学项目，为研究生和本科课程开发新的课程模块，以及专门为少数群体举办暑期讲习班。了解转学表现不佳的真正根本原因是缓解这些问题和实现承诺的转学速度的关键。然而，多个终端系统的参与、动态变化的背景流量以及当今网络基础设施的复杂性使其成为一个复杂而耗时的过程。该项目为大规模的端到端数据传输开发了一个新的异常检测和性能优化框架。该框架有助于预测、了解、诊断和优化当今极端规模的网络基础设施中的广域文件传输。为了实现这一目标，它推导出了基于深度神经网络的预测模型，该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，收集的指标被馈送到异常分类模型中以确定根本原因。一旦确定了性能问题的根本原因，框架将启动实时优化过程来重新配置传输设置，以便可以减轻异常情况的影响。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.