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A smart fairness mechanism for Concurrent multipath transfer in SCTP over wireless multi-hop networks

机译:无线多跳网络中SCTP中并行多径传输的智能公平机制

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The emerging use of multihomed devices in wireless multi-hop networks has increased the demand for multipath transport protocols, such as Concurrent multipath transfer in Stream control transmission protocol (CMT-SCTP). The fairness of CMT-SCTP over wireless multi-hop settings is a critical issue since it can harm the performance of the existing legacy protocols such as Transmission control protocol (TCP) and single-homed SCTP. As such, in this paper, we study the fairness behavior of CMT-SCTP on a multi-hop wireless testbed. The investigation reveals that CMT-SCTP is significantly unfair towards flows coming from farther away hops. Consequently, we introduce a distributed Q-learning mechanism to enhance the fairness of CMT-SCTP association towards other flows. The proposed method uses Reinforcement learning (RL) to acquire knowledge about network dynamics. The acquired knowledge is used to choose the best action to improve the fairness index of the network. We compare our proposal with standard CMT-SCTP and Resource pool CMT-SCTP (CMT/RP-SCTP). Extensive experimentation confirms that our proposal significantly outperforms the available fairness mechanisms for CMT-SCTP. (C) 2016 Elsevier B.V. All rights reserved.
机译:无线多跳网络中多宿主设备的新兴使用增加了对多路径传输协议的需求,例如流控制传输协议(CMT-SCTP)中的并发多路径传输。 CMT-SCTP在无线多跳设置上的公平性是一个关键问题,因为它会损害现有传统协议(例如传输控制协议(TCP)和单宿主SCTP)的性能。因此,在本文中,我们研究了多跳无线测试平台上CMT-SCTP的公平性行为。调查显示,CMT-SCTP对于来自更远啤酒花的流量明显不公平。因此,我们引入了分布式Q学习机制,以增强CMT-SCTP关联对其他流程的公平性。所提出的方法使用强化学习(RL)来获取有关网络动力学的知识。所获得的知识用于选择最佳措施以提高网络的公平性指标。我们将我们的提案与标准CMT-SCTP和资源池CMT-SCTP(CMT / RP-SCTP)进行了比较。大量的实验证实,我们的建议大大优于CMT-SCTP的可用公平机制。 (C)2016 Elsevier B.V.保留所有权利。

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