Efficient dynamic routing and spectrum assignment for multifiber elastic optical networks

Efficient dynamic routing and spectrum assignment for multifiber elastic optical networks
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DOI:
10.1364/jocn.11.000190
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发表时间:
2019-04
期刊:
IEEE/OSA Journal of Optical Communications and Networking
影响因子:
--
通讯作者:
Jingxin Wu;S. Subramaniam;H. Hasegawa
Jingxin Wu;S. Subramaniam;H. Hasegawa
中科院分区:
其他
文献类型:
--
作者:
Jingxin Wu;S. Subramaniam;H. Hasegawa

文献摘要

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弹性光网络通过采用弹性网格光正交频分复用技术,为异构业务提供灵活的带宽分配,是提高频谱利用率的有效解决方案。随着互联网流量的急剧增长和光纤容量即将耗尽,每个链路需要多根光纤来满足不断增长的需求。在本文中,我们研究路径选择和频谱管理异构带宽请求考虑到网络拓扑结构和预期的流量模式。提出了一种新的有效的路径选择和频谱分配的解决方案,优化分配后的网络状态。我们首先开发整数线性规划公式计算离线路径选择概率。然后,我们提出了一个频谱分区方案,提供了一个特定的频谱范围为每个请求的大小。在此基础上,提出了一种基于下一状态感知的频谱分配算法。我们使用动态到达请求的需求阻塞率作为性能指标。仿真结果表明,我们提出的路由和频谱分配方案的有效性,因为他们执行1至2个数量级优于几个基线计划。
Elastic optical networks are seen as a promising solution to improve spectrum utilization efficiency by utilizing flex-grid optical orthogonal frequency division multiplexing technology and facilitating flexible bandwidth allocation to services with heterogeneous demands. With dramatic growth of Internet traffic and imminent fiber capacity exhaustion, multiple fibers per link are required to accommodate increasing demands. In this paper, we investigate path selection and spectrum management for heterogeneous bandwidth requests by taking the network topology and expected traffic pattern into account. A novel and efficient solution for path selection and spectrum assignment that optimizes the state of the network after assignment is proposed. We first develop integer linear programming formulations to calculate path selection probabilities offline. Then, we propose a spectrum partition scheme that provides a particular spectrum range for each request size. A next-state-aware spectrum assignment algorithm with resource sharing among partitions is then introduced. We use the demand blocking ratio for dynamically arriving requests as the performance indicator. Simulation results indicate the effectiveness of our proposed schemes for routing and spectrum assignment, as they perform 1 to 2 orders of magnitude better than several baseline schemes.