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arXiv 1909.03059cs.NIcs.LG
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软件定义网络中目的地感知的自适应业务流规则聚合

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

Technische Universität Chemnitz(开姆尼茨工业大学)

Trung V. Phan, Mehrdad Hajizadeh, Nguyen Tuan Khai, Thomas Bauschert

摘要:

在本文中,我们提出了一个目的地感知的自适应流量规则聚合(DATA)机制,以促进基于SDN的网络中的流量监控。该方法根据其他机制(例如,用于流量工程、流量监控、入侵检测)所需的流量流信息的详细程度来适配SDN交换机中的流表条目的数量。它还通过将流表条目的数量保持在远低于临界水平来防止SDN交换机的性能下降。该水平不是预设为硬阈值,而是在操作期间通过使用基于机器学习的算法来学习。DATA方法在RESTful应用(DATA App)内实现,该RESTful应用监控和分析正在进行的网络流量,并向SDN控制器提供指令以相应地适配流量流匹配策略。在SDN仿真环境中对DATA进行了全面的性能评估。结果表明,与常见SDN控制器的默认行为相比,所提出的DATA方法可以显著提高SDN交换机的性能,同时仍能按需提供详细的流量信息。

英文摘要:

In this paper, we propose a destination-aware adaptive traffic flow rule aggregation (DATA) mechanism for facilitating traffic flow monitoring in SDN-based networks. This method adapts the number of flow table entries in SDN switches according to the level of detail of traffic flow information that other mechanisms (e.g. for traffic engineering, traffic monitoring, intrusion detection) require. It also prevents performance degradation of the SDN switches by keeping the number of flow table entries well below a critical level. This level is not preset as a hard threshold but learned during operation by using a machine-learning based algorithm. The DATA method is implemented within a RESTful application (DATA App) which monitors and analyzes the ongoing network traffic and provides instructions to the SDN controller to adapt the traffic flow matching strategies accordingly. A thorough performance evaluation of DATA is conducted in an SDN emulation environment. The results show that---compared to the default behavior of common SDN controllers---the proposed DATA approach yields significant SDN switch performance improvements while still providing detailed traffic flow information on demand.

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。

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