• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of network and computer applications >ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network
【24h】

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

机译：ACCPndn：通过使用优化的时滞前馈神经网络学习能力来实现命名数据网络中的自适应拥塞控制协议

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in the literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NON (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase - adaptive training - we propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance- we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and high-utilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics. (C) 2015 Elsevier Ltd. All rights reserved.
机译：命名数据网络（NDN）是一种很有前途的网络体系结构，被认为可以替代当前基于IP的Internet基础结构。但是，当在特定时间段内到达一个或多个路由器的数据包的数量超过其队列溢出的数量时，NDN就会出现拥塞。为了解决这个问题，在文献中已经提出了许多拥塞控制协议，但是它们对它们的控制参数高度敏感，并且不能提前足够好地预测拥塞流量。本文通过学习两个阶段的能力来控制拥塞通信，以开始控制网络性能，从而开发出一种NON（ACCPndn）自适应拥塞控制协议。在第一阶段（自适应训练）中，我们提出了一种时滞前馈网络（TLFN），该网络通过粒子群优化和遗传算法的混合进行优化，以预测拥塞的来源以及拥塞的数量。在第二阶段-模糊避免-我们采用基于非线性模糊逻辑的控制系统，根据每个接口每个路由器中第一阶段的结果做出主动决策，以预先控制和/或防止数据包丢失。大量的仿真和结果表明，ACCPndn可以充分满足应用的性能指标，并且在瓶颈链路中的最小数据包丢弃和高利用率（重试替代路径）方面，可以缓解拥塞流量，并且性能优于NACK和HoBHIS等先前的两个建议。 （C）2015 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号