It is challenging to deal with the Internet congestion problem because of several factors such as ever-growing traffic and distributed network architecture. The congestion problem can be solved or alleviated by various methods, including rate control, bandwidth-guarantee routing and bandwidth reservation. We use the term broad-sensed Internet congestion control and avoidance (BICC&A) to generally denote all of the above methods. Most BICC&A solutions depend on or benefit from the knowledge of network conditions, including traffic status (type and volume), available bandwidth and topology. In this paper, we present a comprehensive survey of the applications of machine learning to network condition acquirement methods for BICC&A and specific BICC&A methods. First, we provide an overview of the background knowledge of BICC&A and machine learning. Then, we provide detailed reviews on the applications of machine learning techniques to network condition acquirement methods for BICC&A and to specific BICC&A methods. Finally, we outline important research opportunities.

中文翻译：

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机器学习用于广义的Internet拥塞控制和避免：全面调查

由于流量不断增长和分布式网络体系结构等多种因素，解决Internet拥塞问题具有挑战性。拥塞问题可以通过各种方法来解决或缓解，包括速率控制，带宽保证路由和带宽预留。我们使用广义的互联网拥塞控制和避免（BICC＆A）一词来表示所有上述方法。大多数BICC＆A解决方案都依赖或受益于网络状况的知识，包括流量状态（类型和数量），可用带宽和拓扑。在本文中，我们对机器学习在BICC＆A和特定BICC＆A方法的网络条件获取方法中的应用进行了全面的概述。首先，我们概述BICC＆和机器学习。然后，我们将详细介绍机器学习技术在BICC＆A的网络条件获取方法以及特定BICC＆A方法中的应用。最后，我们概述了重要的研究机会。