一种多域光网络拓扑聚合算法

张永棠

doi:10.13568/j.cnki.651094.2019.01.003

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一种多域光网络拓扑聚合算法

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- 一种多域光网络拓扑聚合算法
- 一种多域光网络拓扑聚合算法
- 新疆大学学报（自然科学版中英文） 2019年36卷第1期页码：17-24
- 作者机构：
  
  1. 广东东软学院计算机科学与技术系
  2. 广东省大数据分析与处理重点实验室
  3. 南昌工程学院江西省协同感知与先进计算技术研究所
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目(61363047);广东省大数据分析与处理重点实验室开放基金(2017007);广东省高校重点平台与特色创新项目(2017KTSCX200);佛山市科技创新项目(2016AG100792)
- DOI：10.13568/j.cnki.651094.2019.01.003
  中图分类号： TN929.1
- 纸质出版：2019
- 稿件说明：
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[1]张永棠.一种多域光网络拓扑聚合算法[J],2019,36(01):17-24.
[1]张永棠.一种多域光网络拓扑聚合算法[J],2019,36(01):17-24. DOI： 10.13568/j.cnki.651094.2019.01.003.

DOI：10.13568/j.cnki.651094.2019.01.003.

摘要

将复杂物理网络拓扑转换为简单的虚拟拓扑聚合是解决大规模多域光网络可扩展性和安全性问题的关键技术.提出了一种新的光网络多域线性阶梯聚合算法(ML-S)

将线性段拟合算法升级为阶梯生成的多线拟合算法.通过查找阶梯的突变点

增加拟合线段的数量

减少冗余

对网络拓扑信息的描述进行改进.此外

ML-S融合了阶梯拟合算法

有效地缓解了拓扑信息的复杂性和准确性之间的矛盾.根据每个域的具体拓扑信息动态地选择一种更精确、更少冗余的算法.仿真结果表明

与最小二乘算法和梯形拟合算法相比

ML-S失真性能指数降低了60%

与多线拟合算法相比

ML-S冗余度降低了50%.在不同的拓扑条件下

ML-S保持了低估计失真、高估计失真和冗余度

在聚集程度和精度之间实现了更好的平衡.

Abstract

Transforming a complex physical network topology into a simple virtual topology aggregation is a key technology to solve the scalability and security issues of large-scale multi-domain optical networks. A new optical network Multiline-Stair(ML-S) aggregation algorithm is proposed

which upgrades the linear segmentfitting algorithm to a multi-line fitting algorithm generated by the ladder. 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 the ML-S distortion performance index is reduced by 60% compared with the least squares algorithm and the trapezoidal fitting algorithm

and the ML-S redundancy is reduced by 50% compared with the multi-line fitting algorithm. 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.

关键词

Keywords

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