带辅助存储的SDM半透明光网络动态路由算法性能研究

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Wojciech Kmiecik, Krzysztof Walkowiak
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引用次数: 2

摘要

在本文中,我们专注于半透明光谱空间柔性光网络(SS FON)中的动态光路供应,该网络结合了光谱(EON)和空间(SDM)灵活性。我们利用距离自适应传输(DAT)以灵活的方式选择请求和路由路径的调制。此外,我们假设信号再生是通过应用在背靠背(B2B)配置中操作的光收发器来实现的。最后,我们提出了一种新的SS FON路由框架,该框架包括使用辅助存储(AS)执行存储和转发(SnF),以减少峰值流量需求并更好地利用网络带宽。我们设计了不同的测试场景,不同的辅助存储可用性,在B2B再生过程中有调制转换和无调制转换,以及不同的现实网络拓扑。为了能够比较测试场景的结果,我们提出了一种动态路由算法,称为具有B2B再生和辅助存储的自适应路由(ARB2BAS),该算法解决了路由、空间和频谱分配(RSSA)优化问题。我们研究了不同的灵活性方面如何影响网络阻塞性能,即带宽阻塞概率(BBP)度量。所获得的结果表明,就阻塞概率而言,算法(场景)的正确选择对网络性能有显著影响。此外,引入辅助存储使网络能够为所有研究的B2B场景和网络拓扑提供更多请求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A performance study of dynamic routing algorithm for SDM translucent optical networks with assistive storage

In this paper, we focus on dynamic lightpath provisioning in translucent spectrally spatially flexible optical networks (SS-FONs) created with a combination of both spectral (EON) and spatial (SDM) flexibility. We utilize the distance-adaptive transmission (DAT) in order to select a modulation for request and routing path in a flexible manner. Moreover, we assume that the signal regeneration is realized with the application of optical transceivers operating in a back-to-back (B2B) configuration. Finally, we propose a novel routing framework for SS-FONs, which involves performing Store-and-Forward (SnF) using Assistive Storage (AS), to reduce peak traffic demand and to better the utilization of network bandwidth. We design different test scenarios, varying in Assistive Storage availability, with and without modulation conversion during the B2B regeneration process and different real-life network topologies. In order to be able to compare the results of test scenarios, we propose a dynamic routing algorithm named adaptive routing with B2B regeneration and Assistive Storage (ARB2BAS) that solves routing, space and spectrum allocation (RSSA) optimization problem. We study how different flexibility aspect affects the network blocking performance, namely the Bandwidth Blocking Probability (BBP) metric. The obtained results show that the proper selection of an algorithm (scenario) has a significant impact on network performance in terms of the blocking probability. Moreover, introducing Assistive Storage enables the network to serve more requests for all of the studied B2B scenarios and network topologies.

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来源期刊
Optical Switching and Networking
Optical Switching and Networking COMPUTER SCIENCE, INFORMATION SYSTEMS-OPTICS
CiteScore
5.20
自引率
18.20%
发文量
29
审稿时长
77 days
期刊介绍: Optical Switching and Networking (OSN) is an archival journal aiming to provide complete coverage of all topics of interest to those involved in the optical and high-speed opto-electronic networking areas. The editorial board is committed to providing detailed, constructive feedback to submitted papers, as well as a fast turn-around time. Optical Switching and Networking considers high-quality, original, and unpublished contributions addressing all aspects of optical and opto-electronic networks. Specific areas of interest include, but are not limited to: • Optical and Opto-Electronic Backbone, Metropolitan and Local Area Networks • Optical Data Center Networks • Elastic optical networks • Green Optical Networks • Software Defined Optical Networks • Novel Multi-layer Architectures and Protocols (Ethernet, Internet, Physical Layer) • Optical Networks for Interet of Things (IOT) • Home Networks, In-Vehicle Networks, and Other Short-Reach Networks • Optical Access Networks • Optical Data Center Interconnection Systems • Optical OFDM and coherent optical network systems • Free Space Optics (FSO) networks • Hybrid Fiber - Wireless Networks • Optical Satellite Networks • Visible Light Communication Networks • Optical Storage Networks • Optical Network Security • Optical Network Resiliance and Reliability • Control Plane Issues and Signaling Protocols • Optical Quality of Service (OQoS) and Impairment Monitoring • Optical Layer Anycast, Broadcast and Multicast • Optical Network Applications, Testbeds and Experimental Networks • Optical Network for Science and High Performance Computing Networks
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