用于估计网络流量的卷积LSTM模型
Convolutional LSTM models to estimate network traffic
摘要：
至少在原则上，网络利用效率通常可以通过动态重新配置路由策略来提高，以更好地分配正在进行的大数据传输。不幸的是，通常缺乏决定适当重新配置所需的信息-正在进行和即将进行的数据传输的详细信息，例如其来源和目的地，最重要的是，其数量和持续时间。幸运的是，越来越多地使用定期转移服务，如FTS，使收集必要的信息成为可能。然而，仅仅检测和表征较大的传输不足以有信心地预测网络链路将变得过载的可能性。在本文中，我们提出了使用基于LSTM的模型（CNN-LSTM和Conv-LSTM）来有效地估计未来的网络流量，从而为制定合理的网络配置计划提供坚实的基础。
英文摘要：
Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute on-going large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration - details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration - is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.
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。
