Design and Simulation of a Topology Aggregation Algorithm in Multi-Domain Optical Networks

Lei Wang, Li Lin, Li Du
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引用次数: 1

Abstract

The aggregate conversion from the complex physical network topology to the simple virtual topology reduces not only load overhead, but also the parameter distortion of links and nodes during the aggregation process, thereby increasing the accuracy of routing. To this end, focusing on topology aggregation of multi-domain optical networks, a new topology aggregation algorithm (ML-S) was proposed. ML-S upgrades linear segment fitting algorithms to multiline fitting algorithms on stair generation. It finds mutation points of stair to increase the number of fitting line segments and makes use of less redundancy, thus obtaining a significant improvement in the description of topology information. In addition, ML-S integrates stair fitting algorithm and effectively alleviates the contradiction between the complexity and accuracy of topology information. It dynamically chooses an algorithm that is more accurate and less redundant according to the specific topology information of each domain. The simulation results show that, under different topological conditions, ML-S maintains a low level of underestimation distortion, overestimation distortion, and redundancy, achieving an improved balance between aggregation degree and accuracy.
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多域光网络拓扑聚合算法的设计与仿真
从复杂的物理网络拓扑到简单的虚拟拓扑的聚合转换,不仅减少了负载开销,而且减少了聚合过程中链路和节点的参数失真,从而提高了路由的准确性。为此,针对多域光网络的拓扑聚合问题,提出了一种新的拓扑聚合算法ML-S。在楼梯生成方面,ML-S将线性段拟合算法升级为多线拟合算法。该方法通过寻找阶梯的突变点来增加拟合线段的数量,并利用较少的冗余,从而在拓扑信息的描述上有了明显的改进。此外,ML-S集成了阶梯拟合算法,有效缓解了拓扑信息复杂性与准确性之间的矛盾。它根据每个域的具体拓扑信息动态选择更精确、冗余度更低的算法。仿真结果表明,在不同的拓扑条件下,ML-S保持了较低的过估计失真、过估计失真和冗余度,实现了聚合度和精度之间更好的平衡。
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