具有预测容量消耗的图路由容错网络中的拥塞建模

E. Birrane
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引用次数: 13

摘要

我们提出了预测容量消耗(PCC),这是一种基于图的路由协议的拥塞建模扩展。这个扩展提供了一个解决方案,以流量控制的问题,在容忍延迟网络(DTNs)和其他覆盖,既不能同步物理链路状态跨网络也不能协商带宽消耗桥接异构链路层。PCC能够构建一个独立于底层链路层的分布式、预测性拥塞模型,而不需要过多的广播或其他在dtn中不可行的机制。PCC检查路由协议生成的信息,并调整本地路由图,以考虑预测的消息路径,纠正下游拥塞和消息重传。与其他机制不同,PCC提供的流量控制可以在任何使用基于图的路由方法的地方实现,采用这种方法只需要对原位路由框架进行少量修改。我们描述了PCC算法,分析了其操作,并通过模拟多个数据流驱动一组约束网络达到饱和来演示其性能。仿真结果表明,PCC比表路由方法提高了97%的网络吞吐量,比没有拥塞模型的图路由方法提高了37%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Congestion modeling in graph-routed Delay Tolerant Networks with Predictive Capacity Consumption
We present Predictive Capacity Consumption (PCC), a congestion modeling extension to graph-based routing protocols. This extension provides a solution to the problem of flow control in Delay-Tolerant Networks (DTNs) and other overlays that can neither synchronize physical link state across the network nor negotiate bandwidth consumption bridging heterogeneous link layers. PCC enables the construction of a distributed, predictive congestion model independent of the underlying link layer without requiring excessive broadcasts or other mechanisms unfeasible in DTNs. PCC examines information generated by routing protocols and adjusts local routing graphs to account for predicted message paths, correcting for downstream congestion and message retransmission. Unlike other mechanisms, the flow control provided by PCC can be implemented anywhere a graph-based routing methodology is used and the adoption of this method requires only minor modification to the in-situ routing framework. We describe the PCC algorithm, analyze its operation, and demonstrate its performance by simulating multiple data streams driving a set of constrained networks to saturation. The simulation results show that PCC improves the throughput of the network by 97% over table routing approaches and by 37% over graph routing approaches without congestion models.
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