弹性光网络分层流量疏导优化方法（特邀）

郝志芃; 杨慧洁; 陈虹; 高明义; 鞠卫国; 陈伯文

doi:10.13756/j.gtxyj.2024.230182

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弹性光网络分层流量疏导优化方法（特邀）

专题：数据中心内光交换 | 更新时间：2024-10-08

- 弹性光网络分层流量疏导优化方法（特邀）
- Stratification Traffic Grooming Methods in Elastic Optical Networks
- 光通信研究 2024年第5期页码：23018201-23018206
- 作者机构：
  
  1.苏州大学　电子信息学院，江苏　苏州　215006
  2.中通服咨询设计研究院有限公司　信息与通信技术研究所，南京　210019
- 作者简介：
  
  郝志芃（1999－），女，河北石家庄人。硕士，主要研究方向为弹性光网络。
  陈伯文，副教授。E-mail：bwchen@suda.edu.cn
- 基金信息：
  
  国家重点研发计划资助项目(2022YFB2903303);江苏省自然科学基金优秀青年资助项目(BK20200099)
- DOI：10.13756/j.gtxyj.2024.230182
  中图分类号： TN929.11
- 纸质出版日期：2024-10-10，
  
  收稿日期：2023-12-24，
  
  修回日期：2024-03-19，
- 稿件说明：
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郝志芃，杨慧洁，陈虹，等. 弹性光网络分层流量疏导优化方法[J]. 光通信研究，2024(5)：230182.

Hao Z P, Yang H J，Chen H，et al, Stratification Traffic Grooming Methods in Elastic Optical Networks[J]. Study on Optical Communications, 2024(5)：230182.
郝志芃，杨慧洁，陈虹，等. 弹性光网络分层流量疏导优化方法[J]. 光通信研究，2024(5)：230182. DOI： 10.13756/j.gtxyj.2024.230182.

Hao Z P, Yang H J，Chen H，et al, Stratification Traffic Grooming Methods in Elastic Optical Networks[J]. Study on Optical Communications, 2024(5)：230182. DOI： 10.13756/j.gtxyj.2024.230182.

摘要

【目的】

随着云计算、大数据和数据中心（DC）等技术的迅猛进步，DC的规模与数量正在持续扩张和增长，网络的带宽资源和能耗也在急速增长。如何有效减少网络带宽资源与能耗受到广泛关注。文章旨在减少网络能耗和提高网络频谱资源利用效率。

【方法】

考虑工作网络能耗元件特点、频谱资源分配方式和不同调制格式选择，文章采用自适应流量疏导方法和频谱分层方式，提出了弹性光网络的距离自适应分层流量疏导方法（DSGM），把频谱资源划分成不同频谱层进行网络流量疏导。一方面，根据距离自适应调制方法选择不同频谱效率的调制格式；另一方面，根据频谱分层流量疏导方式，对每一条光纤的空闲频谱资源按不同速率的光通道进行分层，通过光纤链路剩余带宽资源，来选择合适的光通道进行流量疏导，优化频谱资源的使用。为了方便比较，文章引入了频谱分层流量疏导方法（SSGM）、传统流量疏导方法（TTGM）和没有流量疏导的传统方法（TMWG）。

【结果】

仿真结果表明，与SSGM、TTGM和TWGM相比，文章所提DSGM能明显降低弹性光网络能耗，减少网络的阻塞率，提高资源利用效率。

【结论】

通过文章所提弹性光网络分层流量疏导优化方法，实现了网络能耗与频谱资源优化的目标。

Abstract

【Objective】

With the rapid development of cloud computing

big data technology

and Data Center(DC)

the scale and number of DC continue to expand

and the bandwidth resources and energy consumption of optical networks is also growing rapidly. It can be seen that the method can effectively reduce both bandwidth resources and energy consumption in the network has received widespread attention.

【Methods】

Considering the characteristics of both the network energy consuming components

the spectrum resource allocation

and the selection of different modulation formats

we employ both adaptive traffic grooming method and a spectrum layering method for network traffic grooming. A Distance-adaptive Stratification Grooming Method (DSGM) in elastic optical networks is also proposed. We can employ the adaptive selection of different modulation formats to divide spectrum resources into different spectrum layers for network traffic grooming. In this way

on the one hand

the distance adaptive modulation method can select different modulation formats based on the length of the connection request working path. On the other hand

based on the spectrum layered traffic grooming method

the idle spectrum resources of each fiber link are layered according to different rates of optical channels. The appropriate optical channels are selected for traffic grooming through optimizing the use of spectrum resources based on the remaining bandwidth resources of the fiber links. We also introduce three different traffic grooming methods for comparison

including Spectrum Stratification Grooming Method (SSGM)

Traditional Traffic Grooming Method (TTGM)

and Traditional Method Without Grooming (TMWG).

【Results】

The simulation results show that the proposed approach significantly reduces the energy consumption

and the blocking probability. It also improves the network spectrum efficiency compared with SSGM

TTGM

and TMWG method.

【Conclusion】

We can realize the network resource optimization by using the stratification traffic grooming methods in elastic optical networks.

关键词

数据中心弹性光网络流量疏导距离自适应调制

Keywords

DC elastic optical networktraffic groomingdistance adaptive modulation

references

Xue X W, Calabretta N. Synergistic Switch Control Enabled Optical Data Center Networks[J]. IEEE Communications Magazine, 2022, 60(3): 62-67.

Fan W, Xiao F, Chen X, et al. Efficient Virtual Network Embedding of Cloud-based Data Center Networks into Optical Networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 32(11): 2793-2808.

Xie C, Zhang B. Scaling Optical Interconnects for Hyperscale Data Center Networks[J]. Proceedings of the IEEE, 2022, 110(11): 1699-1713.

Miladic-Tešic S, Markovic G, Nonkovic N, et al. A Metaheuristic Approach for Source Traffic Grooming in Elastic Optical Networks[J]. Transactions on Emerging Telecommunications Technologies, 2021, 32(1): e4097.

Mendinueta J M D, Shinada S, Hirota Y, et al. High-capacity Super-channel-enabled Multi-core Fiber Optical Switching System for Converged Inter/Intra Data Center and Edge Optical Networks[J]. IEEE Journal of Selected Topics in Quantum Electronics, 2020, 26(4): 4501113.

Hosseini Ghazvini S M, Ghaffarpour Rahbar A, Alizadeh B. Load Balancing, Multipath Routing and Adaptive Modulation with Traffic Grooming in Elastic Optical Networks[J]. Computer Networks, 2020, 169: 107081.

Gao C Y, Cankaya H C, Patel A N, et al. Survivable Impairment-aware Traffic Grooming and Regenerator Placement with Connection-level Protection[J]. Journal of Optical Communications and Networking, 2012, 4(3): 259-270.

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