In-Network Machine Learning Using Programmable Network Devices: A Survey

IF 34.4 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Communications Surveys and Tutorials Pub Date : 2023-12-19 DOI:10.1109/COMST.2023.3344351

Changgang Zheng;Xinpeng Hong;Damu Ding;Shay Vargaftik;Yaniv Ben-Itzhak;Noa Zilberman

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引用次数: 0

Abstract

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.

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使用可编程网络设备进行网内机器学习：调查

机器学习被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，不一而足。然而，机器学习也需要大量的处理工作，往往会增加网络和服务器的负载。由可编程网络设备支持的网内计算的引入，使得在网络内运行应用成为可能，从而提供了更高的吞吐量和更低的延迟。不久之后，网内机器学习解决方案开始出现，在网络本身实现了机器学习功能。本调查介绍了网络内机器学习的概念，并提供了全面的分类方法。调查介绍了该技术，并解释了基于可编程网络设备的不同类型的机器学习解决方案。它探讨了在网络内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。网络内机器学习可为云计算和下一代网络带来巨大好处，本调查报告最后还讨论了未来趋势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Surveys and Tutorials COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS

CiteScore

80.20

自引率

2.50%

发文量

审稿时长

6 months

期刊介绍： IEEE Communications Surveys & Tutorials is an online journal published by the IEEE Communications Society for tutorials and surveys covering all aspects of the communications field. Telecommunications technology is progressing at a rapid pace, and the IEEE Communications Society is committed to providing researchers and other professionals the information and tools to stay abreast. IEEE Communications Surveys and Tutorials focuses on integrating and adding understanding to the existing literature on communications, putting results in context. Whether searching for in-depth information about a familiar area or an introduction into a new area, IEEE Communications Surveys & Tutorials aims to be the premier source of peer-reviewed, comprehensive tutorials and surveys, and pointers to further sources. IEEE Communications Surveys & Tutorials publishes only articles exclusively written for IEEE Communications Surveys & Tutorials and go through a rigorous review process before their publication in the quarterly issues. A tutorial article in the IEEE Communications Surveys & Tutorials should be designed to help the reader to become familiar with and learn something specific about a chosen topic. In contrast, the term survey, as applied here, is defined to mean a survey of the literature. A survey article in IEEE Communications Surveys & Tutorials should provide a comprehensive review of developments in a selected area, covering its development from its inception to its current state and beyond, and illustrating its development through liberal citations from the literature. Both tutorials and surveys should be tutorial in nature and should be written in a style comprehensible to readers outside the specialty of the article.