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首页> 外文会议>2019 IEEE International Symposium on Measurements amp; Networking >Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking
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Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking

机译：软件定义网络中基于机器学习和深度学习的流量分类和预测

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摘要

The Internet is constantly growing in size and becoming more complex. The field of networking is thus continuously progressing to cope with this monumental growth of network traffic. While approaches such as Software Defined Networking (SDN) can provide a centralized control mechanism for network traffic measurement, control, and prediction, still the amount of data received by the SDN controller is huge. To process that data, it has recently been suggested to use Machine Learning (ML). In this paper, we review existing proposal for using ML in an SDN context for traffic measurement (specifically, classification) and traffic prediction. We will especially focus on approaches that use Deep learning (DL) in traffic prediction, which seems to have been mostly untapped by existing surveys. Furthermore, we discuss remaining challenges and suggest future research directions.
机译：互联网规模不断增长，日趋复杂。因此，网络领域正在不断发展，以应对网络流量的这种巨大增长。尽管诸如软件定义网络（SDN）之类的方法可以提供用于网络流量测量，控制和预测的集中控制机制，但SDN控制器接收的数据量仍然很大。为了处理该数据，最近建议使用机器学习（ML）。在本文中，我们回顾了有关在SDN上下文中使用ML进行流量测量（特别是分类）和流量预测的现有建议。我们将特别关注在流量预测中使用深度学习（DL）的方法，这似乎在现有调查中尚未得到充分利用。此外，我们讨论了尚存的挑战并提出了未来的研究方向。

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