ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network
ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network
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DOI：
10.1016/j.jnca.2015.05.017
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Amin Karami
中科院分区：
文献类型：
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作者：
Amin Karami
命名数据网络（NDN）是一种很有前途的网络架构，被认为是当前基于ip的互联网基础设施的可能替代品。但是，当在一段时间内到达一个或多个路由器的数据包数量超过其队列溢出时，NDN就会发生拥塞。为了解决这一问题，文献中提出了许多拥塞控制协议，然而，它们对其控制参数高度敏感，并且无法提前足够好地预测拥塞流量。本文提出了一种NDN自适应拥塞控制协议（ACCPndn），通过两阶段的能力学习，在拥塞流量开始影响网络性能之前对其进行控制。在自适应训练阶段，我们提出了一种混合粒子群优化和遗传算法优化的时滞前馈网络（TLFN）来预测拥塞的来源和拥塞的数量。在第二阶段——模糊回避——我们采用基于非线性模糊逻辑的控制系统，根据第一阶段每个路由器每个接口的结果做出主动决策，以提前控制和/或防止数据包丢失。大量的仿真和结果表明，ACCPndn充分满足了应用的性能指标，并且在瓶颈链路的最小丢包和高利用率（重试替代路径）方面优于NACK和HoBHIS等先前的两种方案，以减轻拥塞流量。
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 NDN (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.