Consistency-Aware Weather Disruption-Tolerant Routing in SDN-Based Wireless Mesh Networks
Consistency-Aware Weather Disruption-Tolerant Routing in SDN-Based Wireless Mesh Networks
复制标题
基于 SDN 的无线网状网络中的一致性感知天气中断容忍路由
DOI：
10.1109/tnsm.2018.2795748
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发表时间：
2018
影响因子：
5.3
通讯作者：
L. Wosinska
中科院分区：
文献类型：
--
作者：
Forough Yaghoubi;M. Furdek;A. Rostami;Peter Öhlén;L. Wosinska
无线网络解决方案是回程领域的主要使能技术，容易受到天气干扰的影响，这些干扰可能会大幅降低网络吞吐量和/或延迟，从而影响严格的5G要求。这些影响可以通过由软件定义的网络体系结构实现的集中式重路由来缓解。然而，由于不同交换机之间的异步，不小心的频繁重新配置可能会导致网络状态不一致，从而可能造成拥塞并限制重新路由收益。本文的目的是通过提出一种算法来决定网络流的重路由时间、顺序和路径，以最小化雨干扰下的总数据丢失，该算法考虑了重构过程中的拥塞。在每个时间样本，中央控制器决定是以交换成本(定义为强制拥塞)采用最优路由，还是以吞吐量损失为代价继续使用现有的次优路由。为了在静态场景中找到数据损失最小的最优解，我们建立了一个动态规划问题，该问题充分利用了整个降雨时段的降雨衰减知识。对于未来降雨衰减未知的动态场景，我们提出了一种在线一致性感知重路由算法，称为具有预测的一致性感知重路由算法(CARP)，该算法利用降雨衰落的时间相关性来估计未来的降雨衰减。在合成网络和真实网络上的仿真结果验证了CARP算法的有效性，与贪婪和常规重路由基准测试方法相比，该算法以更少的重路由操作大幅减少了数据丢失并提高了网络吞吐量。
Wireless network solutions, a dominant enabling technology for the backhaul segment, are susceptible to weather disturbances that can substantially degrade network throughput and/or delay, compromising the stringent 5G requirements. These effects can be alleviated by centralized rerouting realized by software defined networking architecture. However, careless frequent reconfigurations can lead to inconsistencies in the network states due to asynchrony between different switches, which can create congestion and limit the rerouting gain. The aim of this paper is to minimize the total data loss during rain disturbance by proposing an algorithm that decides on the timing, the sequence, and the paths for rerouting of network flows considering the imposed congestion during reconfiguration. At each time sample, the central controller decides whether to adopt the optimal routes at a switching cost, defined as the imposed congestion, or to keep using existing, sub-optimal routes at a throughput loss. To find optimal solutions with minimal data loss in a static scenario, we formulate a dynamic programming problem that utilizes perfect knowledge of rain attenuation for the whole rain period. For dynamic scenarios with unknown future rain attenuation, we propose an online consistency-aware rerouting algorithm, called consistency-aware rerouting with prediction (CARP), which uses the temporal correlation of rain fading to estimate future rain attenuation. Simulation results on synthetic and real networks validate the efficiency of our CARP algorithm, substantially reducing data loss and increasing network throughput with a fewer number of rerouting actions compared to a greedy and a regular rerouting benchmarking approaches.