应用MCDM的弹性光网络频谱碎片整理算法

doi:10.19665/j.issn1001-2400.2022.01.016

摘要/Abstract

摘要：

针对弹性光网络频谱碎片导致业务请求阻塞率和带宽阻塞率增高的问题,对弹性光网络频谱碎片产生的原因进行了分析;根据光网络承载业务的特点,从提高频谱利用率的角度提出了基于多准则决策的弹性光网络碎片整理算法。该算法采用多准则决策方法对碎片整理过程中遇到的选择性问题,通过综合考虑各种评价指标做出决策,并进行频谱碎片的整理。在业务路由阶段,算法分为5个阶段,在每个阶段中根据光网络此时的状态,做出最佳决策来进行频谱碎片的整理。每个阶段都会用不同的标签标记不同类型的连接,并根据多准则决策方法所设置的权重对它们进行判断,最后采取最佳方案,使得碎片整理效果达到最佳。为验证这种算法的效果,通过具体实例进行了仿真验证。结果证明,所提算法具有较低的带宽阻塞率(高负载下阻塞率为36%)和较高的频谱利用率(高负载下频谱利用率可达65%),能有效地改善网络高负载情况下的网络请求阻塞率,为实际条件下弹性光网络频谱碎片处理提供了理论参考。

关键词: 弹性光网络, 频谱碎片, 多准则决策, 带宽阻塞率, 频谱利用率

Abstract:

To solve the problem of the increased service request blocking rate and bandwidth blocking rate caused by the fragmentation of the elastic optical network spectrum,the causes of spectrum fragmentation is analyzed in detail.According to the characteristics of optical network carrying services,an elastic optical network fragmentation algorithm based on multi-criteria decision-making is proposed from the perspective of improving spectrum utilization.The algorithm uses the multi-criteria decision-making method to deal with the selectivity problems encountered in the process of defragmentation,and makes decisions by comprehensively considering various evaluation indexes,so as to defragment spectrum fragments.In the traffic routing stage,the algorithm is divided into five stages.In each stage,according to the current state of the optical network,the best decision is made to sort out the spectrum fragments.Each stage marks different types of connections with different labels,and judges them according to the weights set by the multi-criteria decision-making method.Finally,the best scheme is adopted to achieve the best defragmentation effect.Simulation verification is carried out through specific examples,with the results showing that the proposed algorithm has a lower bandwidth blocking rate (36% blocking rate under high load) and high spectrum utilization (up to 65% under high load),which can effectively improve the network request blocking rate under high network load conditions,and provide a theoretical reference for the processing of the spectrum fragmentation of the elastic optical network under actual conditions.

Key words: elastic optical network, spectrum fragmentation, multi-criteria decision-making, bandwidth blocking rate, spectrum utilization

中图分类号:

TN929.11

王鲸鱼,冉金志,王平. 应用MCDM的弹性光网络频谱碎片整理算法[J]. 西安电子科技大学学报, 2022, 49(1): 161-172.

WANG Jingyu,RAN Jinzhi,WANG Ping. Research on the spectrum defragmentation algorithm for the elastic optical network based on MCDM[J]. Journal of Xidian University, 2022, 49(1): 161-172.

图/表 22

图1

图2

图3

图4

图5

图6

图7

图8

表1

表2

表3

表4

表5

表6

表7

表8

图9

图10

图11

图12

图13

图14

参考文献 13

[1]	CHATTERJEE B C, BA S, OKI E. Fragmentation Problems and Management Approaches in Elastic Optical Networks:A Survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(1):183-210.
[2]	ZHANG L, ANSARI N, KHREISHAH A. Anycast Planning in Space Division Multiplexing Elastic Optical Networks with Multi-Core Fibers[J]. IEEE Communications Letters, 2016, 20(10):1983-1986. doi: 10.1109/LCOMM.2016.2593479
[3]	DASILVA E F, ALMEIDA R C, PEREIRA H A, et al. Assessment of Novel Regenerator Assignment Strategies in Dynamic Translucent Elastic Optical Networks[J]. Photonic Network Communications, 2020, 39(1):54-69. doi: 10.1007/s11107-019-00873-9
[4]	IYER S. An Adaptive Model for Spectrum Assignment in Elastic Optical Networks[J]. International Journal of Communication Networks and Distributed Systems, 2020, 24(1):58. doi: 10.1504/IJCNDS.2020.103864
[5]	杨帆, 秦帅, 徐展琦, 等. 节点与链路协作的动态虚拟光网络映射算法[J]. 西安电子科技大学学报, 2018, 45(4):63-68.
	YANG Fan, QIN Shuai, XU Zhanqi, et al. Dynamic Virtual Optical Network Mapping Algorithm with Cooperation Between Nodes and Links[J]. Journal of Xidian University, 2018, 45(4):63-68.
[6]	SAVVA G, MANOUSAKIS K, ELLINAS G. Eavesdropping-Aware Routing and Spectrum/Code Allocation in OFDM-Based EONs Using Spread Spectrum Techniques[J]. Journal of Optical Communications and Networking, 2019, 11(7):409-421. doi: 10.1364/JOCN.11.000409
[7]	刘焕淋, 杜理想, 陈勇, 等. 串扰感知的空分弹性光网络频谱转换器稀疏配置和资源分配方法[J]. 电子与信息学报, 2020, 42(7):1718-1725.
	LIU Huanlin, DU Lixiang, CHEN Yong, et al. Crosstalk-Aware Spectrum Converters Sparse Configuration and Resource Allocation for Space Division Multiplexing Elastic Optical Networks[J]. Journal of Electronics and Information Technology, 2020, 42(7):1718-1725.
[8]	YOUSEFI F, RAHBAR A, YAGHUBI-NAMAAD M. Fragmentation-Aware Algorithms for Multipath Routing and Spectrum Assignment in Elastic Optical Networks[J]. Optical Fiber Technology, 2019, 53:1-18.
[9]	ZHANG M, YOU C, ZHU Z. On The Parallelization of Spectrum Defragmentation Reconfigurations in Elastic Optical Networks[J]. IEEE/ACM Transactions on Networking, 2016, 24(5):2819-2833. doi: 10.1109/TNET.2015.2487366
[10]	孙伟伟. 弹性光网络非中断频谱碎片整理方法综述[J]. 安徽电子信息职业技术学院学报, 2019, 18(2):33-36.
	SUN Weiwei. Overview of the Hitless Spectrum Defragmentation in Elasto-Optical Networks[J]. Journalof Anhui Vocational College of Electronics & Information Technology, 2019, 18(2):33-36.
[11]	WANG X, DONG Z, AI X, et al. Multi-Objective Model and Decision-Making Method for Coordinating the Ecological Benefits of the Three Gorger Reservoir[J]. Journal of Cleaner Production, 2020, 270:122066. doi: 10.1016/j.jclepro.2020.122066
[12]	罗彬珅, 刘利民, 董健, 等. 基于多准则融合的雷达干扰源个体特征选择[J]. 电光与控制, 2020, 27(5):19-24.
	LUO Binshen, LIU Limin, DONG Jian, et al. Multi-Criteria Fusion Based Individual Feature Selection of Radar Jamming Emitter[J]. Electronics Optics & Control, 2020, 27(5):19-24.
[13]	张正昱, 金贵, 郭柏枢, 等. 基于多准则决策的长江经济带国土空间脆弱性与恢复力研究[J]. 自然资源学报, 2020, 35(1):95-105. doi: 10.31497/zrzyxb.20200109
	ZHANG Zhengyu, JIN Gui, GUO Baishu, et al. Research on the Spatial Vulnerability and Resilience of Land in the Yangtze River Economic Belt Based on Multi-Criteria Decision[J]. Journal of Natural Resources, 2020, 35(1):95-105. doi: 10.31497/zrzyxb.20200109

指标	I_x(+)	T_x(+)	S_x(+)	L_x(+)	R_x(-)
I_x	1.00	2.00	2.50	3.33	3.33
T_x	0.50	1.00	1.25	1.66	1.66
S_x	0.40	0.80	1.00	1.33	1.33
L_x	0.30	0.60	0.75	1.00	1.00
R_x	0.30	0.60	0.75	1.00	1.00
总和	2.50	5.00	6.25	8.33	8.33

指标	I_x(+)	I_x(+)	S_x(+)	L_x(+)	R_x(-)
I_x	0.40	0.40	0.40	0.40	0.40
T_x	0.20	0.20	0.20	0.20	0.20
S_x	0.16	0.16	0.16	0.16	0.16
L_x	0.12	0.12	0.12	0.12	0.12
R_x	0.12	0.12	0.12	0.12	0.12