公交时延容忍网络中基于索引的多级分组路由算法

在由以公共交通系统中的车辆为节点构成的无线网络中,由于其中节点的高速移动造成拓扑的快速变化,网络连接也多以瞬时的短暂连接为主.这构成了时延容忍网络(delay tolerant networks,DTN)的一个典型应用场景.公交节点的特性决定了其移动方式(时间、路线)带有一定的规律性.基于这一特性,构建了一种抽象的网络拓扑模型,并基于该模型提出了一种基于索引的多级分组路由算法.实验证明,基于预先的分组信息,该路由算法应用于高速移动的公交时延容忍网络中将比其他DTN路由更加的高效.     

DTN中基于生命游戏的拥塞控制策略

为了应对容迟网络中拓扑结构剧烈变化、节点间连接频繁中断等问题,报文通常采用“存储—携带—转发”的方式进行传输：节点将报文存储在缓存中,携带报文直到遇到合适的机会才将报文转发给其他节点.因为缓存有限,这样的传输方式会使节点缓存溢出,导致拥塞的发生.在容迟网络环境下提出一种基于生命游戏的拥塞控制策略(game of life based congestion control strategy in delay tolerant networks, GLCCS),并将其应用于Epidemic路由方式.GLCCS借鉴生命游戏的演化思想,依据邻居节点中持有特定报文的节点比例来决定节点本地缓存中相应报文的操作.同时还提出了基于全网信息的报文排队机制和丢弃策略,依据传递或者丢弃一个报文对整个网络投递成功率的影响,计算出报文的效用值,按照效用值对缓存中报文进行排队和丢弃.在机会网络模拟器ONE中对仿真移动模型和真实运动轨迹进行模拟,实验结果表明,GLCCS与其他拥塞控制策略相比提高了投递成功率,减小了网络时延、丢包率以及负载比率.     

数字孪生网络(DTN): 概念、架构及关键技术

随着5G商用规模部署、下一代互联网IPv6的深化应用, 新一代网络技术的发展引发产业界的关注. 网络的智能化被认为是新一代网络发展的趋势. 网络为数字化社会的信息传输提供了基础, 而网络本身的数字化是智能化发展的先决条件. 面向数字化、智能化的新一代网络发展目标, 本文首次系统化提出了 “数字孪生网络(DTN: Digital twin network)” 的概念, 给出了系统架构设计, 分析了DTN的关键技术. 通过对DTN发展挑战的分析, 本文指出了未来 “数字孪生网络” 的发展方向.     

An evolutive framework for server placement optimization to digital twin networks

Digital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real-time data and requires interaction with the physical network in real-time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP-hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline-learning-based approach is used to reduce the search space, and a self-examining guided local search method is proposed to improve the search efficiency. Additionally, a look-up table-based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.