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A distributed learning automata based gateway load balancing algorithm in Wireless Mesh Networks

机译:无线网状网络中基于分布式学习自动机的网关负载均衡算法

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Wireless Mesh Networks (WMNs) are a rapidly maturing technology for providing high bandwidth broadband service to a large community of users. In WMNs, gateway nodes act as a central point of connectivity to the wired infrastructure (typically the Internet). Therefore traffic aggregation occurs in the paths leading to a gateway and due to the limited wireless link capacity, these nodes are likely to be potential bottlenecks. In this paper, we propose a distributed load balancing algorithm to achieve load balancing on gateway nodes which leads to efficient traffic allocation as well as maximum use of network capacity. This algorithm uses Learning Automata in order to select the appropriate gateway node to send traffic. Evaluation results demonstrate that the proposed scheme largely avoids congestion and can effectively balance the traffic.
机译:无线网状网络(WMN)是一种快速成熟的技术,用于为广大用户社区提供高带宽宽带服务。在WMN中,网关节点充当与有线基础结构(通常是Internet)的连接的中心点。因此,流量聚合会在通往网关的路径中发生,并且由于有限的无线链路容量,这些节点可能会成为潜在的瓶颈。在本文中,我们提出了一种分布式负载平衡算法,以实现网关节点上的负载平衡,从而实现有效的流量分配以及最大程度地利用网络容量。该算法使用学习自动机以选择适当的网关节点来发送流量。评估结果表明,该方案在很大程度上避免了拥塞,可以有效地平衡流量。

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