使用预训练生成模型进行实时网络延迟估计
概括
我们开发了一种预训练生成模型 (PGM),用于快速估计网络延迟. 这种方法可以在50毫秒内实现准确的实时延迟估计,改善网络性能监控.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 准确的网络延迟估计对于性能监控和管理至关重要.
- 当前的方法与现代网络的实时需求作斗争.
研究的目的:
- 引入一种用于即时网络延迟估计的新方案.
- 为了解决现有的延迟估计技术的局限性.
主要方法:
- 提出了一个两阶段预训练的基于生成模型的方案 (PGM).
- 利用预训练的生成模型来放松低级限制在延迟矩阵完成.
- 为提高效率,优化了缩小的潜伏表示,而不是完整的矩阵.
主要成果:
- 在50毫秒内实现了准确的延迟估计.
- 在PlanetLab数据集上保持了不超过0.11的相对平方误差 (RSE).
- 在真实世界的网络数据上证明了PGM的有效性.
结论:
- PGM 能够有效地进行实时网络延迟估计.
- 拟议的方法比现有技术提供了显著的改进.
- 理论上的保证支持了PGM计划的误差极限.
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