揭秘高性能科研网络中数据传输的性能表现
Demystifying the Performance of Data Transfers in High-Performance Research Networks
AI总结：
针对高性能科研网络数据传输性能不达标的问题，提出可扩展的端到端监测框架及启发式根因分析方法，实现高并发高精度监测与高效异常定位。
摘要：
高速科研网络的构建是为了满足数据密集型分布式工作流不断增长的需求。然而，这些网络中的数据传输往往无法达到承诺的传输速率，原因包括I/O与网络干扰、服务器配置错误以及网络异常等多种因素。尽管明确性能问题的根本原因对于缓解问题、提升昂贵网络基础设施的利用率至关重要，但目前尚无可用机制来监测这类网络中的数据传输。本文提出了一种可扩展的端到端监测框架，用于采集并存储文件传输的关键性能指标，以阐明传输性能的实际情况。评估结果显示，该框架每台主机最多可监测400次传输，总监测传输量超过40000次，同时能以1秒的精度采集性能统计数据。我们还提出了一种启发式方法，可自动处理采集到的性能指标并识别性能异常的根本原因，其F-score达到87%-98%。
英文摘要：
High-speed research networks are built to meet the ever-increasing needs of data-intensive distributed workflows. However, data transfers in these networks often fail to attain the promised transfer rates for several reasons, including I/O and network interference, server misconfigurations, and network anomalies. Although understanding the root causes of performance issues is critical to mitigating them and increasing the utilization of expensive network infrastructures, there is currently no available mechanism to monitor data transfers in these networks. In this paper, we present a scalable, end-to-end monitoring framework to gather and store key performance metrics for file transfers to shed light on the performance of transfers. The evaluation results show that the proposed framework can monitor up to 400 transfers per host and more than 40, 000 transfers in total while collecting performance statistics at one-second precision. We also introduce a heuristic method to automatically process the gathered performance metrics and identify the root causes of performance anomalies with an F-score of 87 - 98%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
