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An Intelligent Packet Forwarding Approach for Disaster Recovery Networks

机译：灾难恢复网络的智能分组转发方法

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摘要

Disasters, such as earthquakes, typhoons, and tsunamis, usually cause extreme damages to the communication infrastructures, which results in a heavy recovery workload and seriously affects people's life. The disaster recovery networks play a critical role to reduce the loss caused by the disasters. However, the suddenly varying traffic demand and limited resources after disasters may lead to the repetitive reconfigurations for running the existing packet forwarding strategies, such as the shortest path algorithms. To handle this problem, it is necessary to adopt the deep learning technique to develop a disaster-resilient solution. In this paper, we utilize the deep reinforcement learning technique to propose a self-adaptive routing method for the Movable and Deployable Resource Unit (MDRU) based backbone network. Compared with existing deep learning based routing strategy, our proposal can adapt to the sudden network errors. Moreover, we also analyze the deployment manner and consider a centralized control structure to significantly balance the traffic.
机译：地震，台风和海啸等灾难通常会对通信基础设施造成极大损害，从而导致沉重的恢复工作量并严重影响人们的生活。灾难恢复网络在减少灾难造成的损失方面起着至关重要的作用。但是，灾难后突发的流量需求和有限的资源可能导致运行现有数据包转发策略（例如最短路径算法）的重复性重新配置。为了解决这个问题，有必要采用深度学习技术来开发具有灾难恢复能力的解决方案。在本文中，我们利用深度强化学习技术为基于可移动和可部署资源单元（MDRU）的骨干网络提出了一种自适应路由方法。与现有的基于深度学习的路由策略相比，我们的建议可以适应突发的网络错误。此外，我们还分析了部署方式，并考虑了集中控制结构以显着平衡流量。

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