Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires significant processing and often increases the load on both networks and servers. The introduction of in-network computing, enabled by programmable network devices, has allowed to run applications within the network, providing higher throughput and lower latency. Soon after, in-network machine learning solutions started to emerge, enabling machine learning functionality within the network itself. This survey introduces the concept of in-network machine learning and provides a comprehensive taxonomy. The survey provides an introduction to the technology and explains the different types of machine learning solutions built upon programmable network devices. It explores the different types of machine learning models implemented within the network, and discusses related challenges and solutions. In-network machine learning can significantly benefit cloud computing and next-generation networks, and this survey concludes with a discussion of future trends.

中文翻译：

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使用可编程网络设备的网络内机器学习：调查


机器学习广泛用于解决网络挑战，从流量分类和异常检测到网络配置。然而，机器学习还需要大量处理，并且通常会增加网络和服务器的负载。由可编程网络设备支持的网络内计算的引入允许在网络内运行应用程序，从而提供更高的吞吐量和更低的延迟。不久之后，网络内机器学习解决方案开始出现，在网络本身内实现机器学习功能。这项调查介绍了网络内机器学习的概念并提供了全面的分类法。该调查介绍了该技术，并解释了基于可编程网络设备构建的不同类型的机器学习解决方案。它探讨了网络中实施的不同类型的机器学习模型，并讨论了相关的挑战和解决方案。网络内机器学习可以极大地有益于云计算和下一代网络，本次调查最后讨论了未来趋势。