Yidi Wang;Chunyu Zhang;Jin Li;Yue Pang;Lifang Zhang;Min Zhang;Danshi Wang
The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.
{"title":"AlarmGPT: an intelligent alarm analyzer for optical networks using a generative pre-trained transformer","authors":"Yidi Wang;Chunyu Zhang;Jin Li;Yue Pang;Lifang Zhang;Min Zhang;Danshi Wang","doi":"10.1364/JOCN.521913","DOIUrl":"10.1364/JOCN.521913","url":null,"abstract":"The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 6","pages":"681-694"},"PeriodicalIF":5.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, multi-band elastic optical networks (MB-EONs) have been considered a viable solution to increase the transmission bandwidth in optical networks. To improve spectral efficiency and reduce the blocking ratio, the general signal-to-noise ratio (GSNR) as a quality-of-transmission (QoT) metric must be accurately calculated in the routing, modulation level, and spectrum assignment algorithms used in elastic optical networks (EONs). The interference prediction methods commonly used for single-band EONs are not efficient in the case of MB-EONs because of the inter-channel stimulated Raman scattering impact and their wide spectrum. In this paper, we propose a statistical method to predict the interference noise in C+L-band EONs considering multi-period planning. The proposed algorithm, which utilizes the predicted total number of channels (PTNC) on each link for given requests, is a low-margin, fast, and cost-effective method. Additionally, the proposed PTNC algorithm can also be used for single-period planning. Our simulation results indicate that the proposed PTNC algorithm combines the advantages of both studied benchmark algorithms. It has a low complexity order and execution time that are comparable to those of the fully loaded algorithm, which is currently employed by the network operators. However, this benchmark does not achieve the best spectral efficiency. Furthermore, the PTNC method and the other benchmark that determines margin through an exhaustive search, referred to as margin exhaustive search (MES), achieve remarkable spectral efficiency and residual capacity with fewer transceivers, resulting in lower capital expenditure requirements. Nevertheless, the MES algorithm may not be practical due to the requirement of reconfiguring established lightpaths and its high complexity order, particularly in multi-period planning.
{"title":"Efficient statistical QoT-aware resource allocation in EONs over the C+L-band: a multi-period and low-margin perspective","authors":"Mahdieh Mehrabi;Hamzeh Beyranvand;Mohammad Javad Emadi;Farhad Arpanaei","doi":"10.1364/JOCN.515081","DOIUrl":"10.1364/JOCN.515081","url":null,"abstract":"Recently, multi-band elastic optical networks (MB-EONs) have been considered a viable solution to increase the transmission bandwidth in optical networks. To improve spectral efficiency and reduce the blocking ratio, the general signal-to-noise ratio (GSNR) as a quality-of-transmission (QoT) metric must be accurately calculated in the routing, modulation level, and spectrum assignment algorithms used in elastic optical networks (EONs). The interference prediction methods commonly used for single-band EONs are not efficient in the case of MB-EONs because of the inter-channel stimulated Raman scattering impact and their wide spectrum. In this paper, we propose a statistical method to predict the interference noise in C+L-band EONs considering multi-period planning. The proposed algorithm, which utilizes the predicted total number of channels (PTNC) on each link for given requests, is a low-margin, fast, and cost-effective method. Additionally, the proposed PTNC algorithm can also be used for single-period planning. Our simulation results indicate that the proposed PTNC algorithm combines the advantages of both studied benchmark algorithms. It has a low complexity order and execution time that are comparable to those of the fully loaded algorithm, which is currently employed by the network operators. However, this benchmark does not achieve the best spectral efficiency. Furthermore, the PTNC method and the other benchmark that determines margin through an exhaustive search, referred to as margin exhaustive search (MES), achieve remarkable spectral efficiency and residual capacity with fewer transceivers, resulting in lower capital expenditure requirements. Nevertheless, the MES algorithm may not be practical due to the requirement of reconfiguring established lightpaths and its high complexity order, particularly in multi-period planning.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"577-592"},"PeriodicalIF":5.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Shakespear-Miles;Q. Lin;S. Barzegar;M. Ruiz;X. Chen;L. Velasco
Optical point-to-multipoint (P2MP) connectivity based on digital subcarrier multiplexing (DSCM) has been shown as a solution for the metro-access segment that is able to reduce capital and operational costs and support the capacity and high dynamicity needs of future 6G services. To achieve maximum performance, activation and deactivation of subcarriers must be done near-real-time to provide just the capacity needed to support the input traffic. In this paper, we investigate the applicability of various approaches capable of supporting the near-real-time operation requirement. Starting from the centralized approach that can be carried out on the centralized software-defined networking (SDN) controller, we also explore distributed approaches that might relieve the SDN controller from near-real-time operation. In particular, we explore the performance of deploying a multiagent system (MAS), where intelligent agents run on top of the nodes in the P2MP tree and communicate among them. Illustrative results show that the distributed approaches can achieve a performance close to that of the centralized one, while reducing communication needs. Results also show the importance of traffic/capacity prediction to anticipate the activation of subcarriers.
基于数字子载波复用（DSCM）的光点对点多点（P2MP）连接已被证明是城域接入网段的一种解决方案，能够降低资本和运营成本，支持未来 6G 服务对容量和高动态性的需求。为了实现最高性能，必须近乎实时地激活和停用子载波，以提供支持输入流量所需的容量。在本文中，我们研究了能够支持近实时操作要求的各种方法的适用性。从可在集中式软件定义网络（SDN）控制器上执行的集中式方法开始，我们还探索了可减轻 SDN 控制器近实时操作负担的分布式方法。特别是，我们探索了部署多代理系统（MAS）的性能，在该系统中，智能代理运行在 P2MP 树中的节点之上，并在它们之间进行通信。示例结果表明，分布式方法可以实现接近集中式方法的性能，同时减少通信需求。结果还显示了流量/容量预测对预测子载波激活的重要性。
{"title":"Centralized and distributed approaches to control optical point-to-multipoint systems near-real-time","authors":"H. Shakespear-Miles;Q. Lin;S. Barzegar;M. Ruiz;X. Chen;L. Velasco","doi":"10.1364/JOCN.516137","DOIUrl":"10.1364/JOCN.516137","url":null,"abstract":"Optical point-to-multipoint (P2MP) connectivity based on digital subcarrier multiplexing (DSCM) has been shown as a solution for the metro-access segment that is able to reduce capital and operational costs and support the capacity and high dynamicity needs of future 6G services. To achieve maximum performance, activation and deactivation of subcarriers must be done near-real-time to provide just the capacity needed to support the input traffic. In this paper, we investigate the applicability of various approaches capable of supporting the near-real-time operation requirement. Starting from the centralized approach that can be carried out on the centralized software-defined networking (SDN) controller, we also explore distributed approaches that might relieve the SDN controller from near-real-time operation. In particular, we explore the performance of deploying a multiagent system (MAS), where intelligent agents run on top of the nodes in the P2MP tree and communicate among them. Illustrative results show that the distributed approaches can achieve a performance close to that of the centralized one, while reducing communication needs. Results also show the importance of traffic/capacity prediction to anticipate the activation of subcarriers.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"565-576"},"PeriodicalIF":5.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.
{"title":"Biphase routing scheme for optimal throughput in large-scale optical satellite networks","authors":"Yunxiao Ning;Yongli Zhao;Avishek Nag;Hua Wang;Jie Zhang","doi":"10.1364/JOCN.514819","DOIUrl":"10.1364/JOCN.514819","url":null,"abstract":"In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"553-564"},"PeriodicalIF":5.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhiming Sun;Chunyu Zhang;Min Zhang;Bing Ye;Danshi Wang
In optical networks, reliable failure detection is essential for maintaining quality of service. The methodology has evolved from traditional performance threshold-driven approaches to contemporary data-driven AI algorithms, predominantly employing supervised and unsupervised learning. However, with the advent of second-level telemetry, optical transport networks have amassed a wealth of unlabeled performance data, while labeled data remains limited due to the intensive effort required for annotation. In this scenario, to address the challenges of scarce labeled data in supervised learning and the accuracy issues in unsupervised methods, we propose an OpenFE-VIME semi-supervised model. This model synergizes the robustness of supervised approaches with the flexibility of unsupervised approaches. It not only leverages the abundant reservoir of unlabeled data but also addresses the challenges posed by the limited availability of labeled data, enabling reliable and efficient failure detection. Upon evaluation using performance data from OTN node devices in the operator’s optical backbone network, the OpenFE-VIME model demonstrates remarkable performance, achieving an F1-score of 0.947 and accuracy of 0.946, while significantly reducing false negative and false positive rates to 0.073 and 0.035, respectively. Moreover, our research explores the model’s capabilities in utilizing both labeled and unlabeled data and investigates the threshold for training convergence across various data ratios. Additionally, the model’s internal mechanisms and decision-making processes are interpreted using t-SNE visualization, offering enhanced insights into its operational efficacy.
{"title":"Semi-supervised learning model synergistically utilizing labeled and unlabeled data for failure detection in optical networks","authors":"Zhiming Sun;Chunyu Zhang;Min Zhang;Bing Ye;Danshi Wang","doi":"10.1364/JOCN.516128","DOIUrl":"10.1364/JOCN.516128","url":null,"abstract":"In optical networks, reliable failure detection is essential for maintaining quality of service. The methodology has evolved from traditional performance threshold-driven approaches to contemporary data-driven AI algorithms, predominantly employing supervised and unsupervised learning. However, with the advent of second-level telemetry, optical transport networks have amassed a wealth of unlabeled performance data, while labeled data remains limited due to the intensive effort required for annotation. In this scenario, to address the challenges of scarce labeled data in supervised learning and the accuracy issues in unsupervised methods, we propose an OpenFE-VIME semi-supervised model. This model synergizes the robustness of supervised approaches with the flexibility of unsupervised approaches. It not only leverages the abundant reservoir of unlabeled data but also addresses the challenges posed by the limited availability of labeled data, enabling reliable and efficient failure detection. Upon evaluation using performance data from OTN node devices in the operator’s optical backbone network, the OpenFE-VIME model demonstrates remarkable performance, achieving an F1-score of 0.947 and accuracy of 0.946, while significantly reducing false negative and false positive rates to 0.073 and 0.035, respectively. Moreover, our research explores the model’s capabilities in utilizing both labeled and unlabeled data and investigates the threshold for training convergence across various data ratios. Additionally, the model’s internal mechanisms and decision-making processes are interpreted using t-SNE visualization, offering enhanced insights into its operational efficacy.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"541-552"},"PeriodicalIF":5.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The computing power network (CPN) is a novel network technology that integrates computing power from the cloud, edge, and terminals using IP/optical cross-layer networks for distributed computing. CPNs can provide an effective solution for distributed model training (DMT). As a bandwidth optimization architecture based on data parallelism, ring all-reduce (RAR) is widely used in DMT. However, any node or link failure on the ring can interrupt or block the requests deployed on the ring. Meanwhile, due to the resource competition of batch RAR-based DMT requests, inappropriate scheduling strategies will also lead to low training efficiency or congestion. As far as we know, there is currently no research that considers the survivability of rings in scheduling strategies for RAR-based DMT. To fill this gap, we propose a scheduling scheme for RAR-based DMT requests in CPNs to optimize the allocation of computing and wavelength resources considering the time dimension while ensuring reliability. In practical scenarios, service providers may focus on different performance metrics. We formulate an integer linear programming (ILP) model and a RAR-based DMT deployment algorithm (RDDA) to solve this problem considering four optimization objectives under the premise of the minimum blocking rate: minimum computing resource consumption, minimum wavelength resource consumption, minimum training time, and maximum reliability. Simulation results demonstrate that our model satisfies the reliability requirements while achieving corresponding optimal performance for DMT requests under four optimization objectives.
{"title":"Reliable and efficient RAR-based distributed model training in computing power network","authors":"Ling Chen;Yajie Li;Carlos Natalino;Yongcheng Li;Boxin Zhang;Yingbo Fan;Wei Wang;Yongli Zhao;Jie Zhang","doi":"10.1364/JOCN.511165","DOIUrl":"10.1364/JOCN.511165","url":null,"abstract":"The computing power network (CPN) is a novel network technology that integrates computing power from the cloud, edge, and terminals using IP/optical cross-layer networks for distributed computing. CPNs can provide an effective solution for distributed model training (DMT). As a bandwidth optimization architecture based on data parallelism, ring all-reduce (RAR) is widely used in DMT. However, any node or link failure on the ring can interrupt or block the requests deployed on the ring. Meanwhile, due to the resource competition of batch RAR-based DMT requests, inappropriate scheduling strategies will also lead to low training efficiency or congestion. As far as we know, there is currently no research that considers the survivability of rings in scheduling strategies for RAR-based DMT. To fill this gap, we propose a scheduling scheme for RAR-based DMT requests in CPNs to optimize the allocation of computing and wavelength resources considering the time dimension while ensuring reliability. In practical scenarios, service providers may focus on different performance metrics. We formulate an integer linear programming (ILP) model and a RAR-based DMT deployment algorithm (RDDA) to solve this problem considering four optimization objectives under the premise of the minimum blocking rate: minimum computing resource consumption, minimum wavelength resource consumption, minimum training time, and maximum reliability. Simulation results demonstrate that our model satisfies the reliability requirements while achieving corresponding optimal performance for DMT requests under four optimization objectives.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"527-540"},"PeriodicalIF":5.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mario Wenning;Maria Samonaki;Sai Kireet Patri;Tobias Fehenberger;Helmut Griesser;Carmen Mas-Machuca
Quantum key distribution (QKD) has become a promising option for transmitting sensitive data due to the increased maturity of QKD devices and the threat scalable quantum computers imposes on asymmetric public-key cryptosystems. By utilizing existing infrastructure, e.g., amplifier huts and dark fibers, the cost and complexity of deploying QKD networks (QKDNs) can be reduced. In this study, we develop a topology optimization algorithm that minimizes the cost of the QKDN deployment and maximizes the key capacity between any pair of nodes. We present three deployment upgrade strategies for facilitating the encryption of increasing throughput of QKD-secured optical transport networks and enabling a pay-as-you-grow approach. Comparing different strategies in a capacity-planning study allows operators to assess the scalability of deployments and upgrades. Depending on the availability requirements, our results compare the cost of protection measures. We confirm that adding trusted nodes is the most cost-efficient upgrading strategy based on our analysis.
{"title":"Upgrading strategies for long-haul quantum key distribution networks","authors":"Mario Wenning;Maria Samonaki;Sai Kireet Patri;Tobias Fehenberger;Helmut Griesser;Carmen Mas-Machuca","doi":"10.1364/JOCN.513632","DOIUrl":"10.1364/JOCN.513632","url":null,"abstract":"Quantum key distribution (QKD) has become a promising option for transmitting sensitive data due to the increased maturity of QKD devices and the threat scalable quantum computers imposes on asymmetric public-key cryptosystems. By utilizing existing infrastructure, e.g., amplifier huts and dark fibers, the cost and complexity of deploying QKD networks (QKDNs) can be reduced. In this study, we develop a topology optimization algorithm that minimizes the cost of the QKDN deployment and maximizes the key capacity between any pair of nodes. We present three deployment upgrade strategies for facilitating the encryption of increasing throughput of QKD-secured optical transport networks and enabling a pay-as-you-grow approach. Comparing different strategies in a capacity-planning study allows operators to assess the scalability of deployments and upgrades. Depending on the availability requirements, our results compare the cost of protection measures. We confirm that adding trusted nodes is the most cost-efficient upgrading strategy based on our analysis.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"B81-B90"},"PeriodicalIF":5.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An ML-supported diagnostics concept is introduced and demonstrated to detect and classify events on OTDR traces for application on a PON optical distribution network. We can also associate events with ODN branches by using deployment data of the PON. We analyze an ensemble classifier and neural networks, the usage of synthetic OTDR-like traces, and measured data for training. In our proof-of-concept, we show a precision of 98% and recall of 95% using an ensemble classifier on measured OTDR traces and a successful mapping to ODN branches or groups of branches. For emulated data, we achieve an average precision of 70% and an average recall of 91%.
{"title":"ML approaches for OTDR diagnoses in passive optical networks—event detection and classification: ways for ODN branch assignment","authors":"Michael Straub;Johannes Reber;Tarek Saier;Robert Borkowski;Shi Li;Dmitry Khomchenko;Andre Richter;Michael Farber;Tobias Kafer;Rene Bonk","doi":"10.1364/JOCN.516659","DOIUrl":"10.1364/JOCN.516659","url":null,"abstract":"An ML-supported diagnostics concept is introduced and demonstrated to detect and classify events on OTDR traces for application on a PON optical distribution network. We can also associate events with ODN branches by using deployment data of the PON. We analyze an ensemble classifier and neural networks, the usage of synthetic OTDR-like traces, and measured data for training. In our proof-of-concept, we show a precision of 98% and recall of 95% using an ensemble classifier on measured OTDR traces and a successful mapping to ODN branches or groups of branches. For emulated data, we achieve an average precision of 70% and an average recall of 91%.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 7","pages":"C43-C50"},"PeriodicalIF":5.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This JOCN special issue contains extended versions of selected papers presented at the 27th International Conference on Optical Network Design and Modeling (ONDM 2023), which took place on 8–11 May 2023 at the University of Coimbra, Coimbra, Portugal. The articles in this special issue contain several current topics of optical networking research: quality of transmission (QoT) estimation and its importance in network control and optimization, strategies to reduce power consumption in optical networks, analysis of optical network resilience from the link level up to inter-carrier networks, and strategies to upgrade long-haul quantum key distribution networks.
{"title":"Introduction to the ONDM 2023 special issue","authors":"Teresa Gomes;David Larrabeiti-Lopez;Carmen Mas-Machuca;Luca Valcarenghi","doi":"10.1364/JOCN.525183","DOIUrl":"https://doi.org/10.1364/JOCN.525183","url":null,"abstract":"This JOCN special issue contains extended versions of selected papers presented at the 27th International Conference on Optical Network Design and Modeling (ONDM 2023), which took place on 8–11 May 2023 at the University of Coimbra, Coimbra, Portugal. The articles in this special issue contain several current topics of optical networking research: quality of transmission (QoT) estimation and its importance in network control and optimization, strategies to reduce power consumption in optical networks, analysis of optical network resilience from the link level up to inter-carrier networks, and strategies to upgrade long-haul quantum key distribution networks.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 5","pages":"ONDM1-ONDM2"},"PeriodicalIF":5.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499924","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jasper Muller;Gabriele Di Rosa;Ognjen Jovanovic;Mario Wenning;Achim Autenrieth;Jorg-Peter Elbers;Carmen Mas-Machuca
Flexible-grid elastic optical networks (EONs) have recently been widely deployed to support the growing demand for bandwidth-intensive applications. For cost-efficient scaling of the network capacity, multi-band systems are a promising solution. Optimized utilization of EONs is required to delay cost-extensive network upgrades and to lower cost and power consumption. Next-generation bandwidth-variable transceivers (BVTs) will offer increased adaptivity in symbol rate and modulation through techniques such as probabilistic shaping (PS). In this work, we investigate the impact of increased configuration granularity on optical networks. We account for practical implementation considerations of BVT configurations for estimating the required signal-to-noise ratio. Additionally, an optimization algorithm is presented that selects the most efficient configuration for each considered data rate and bandwidth combination. We utilize advanced quality of transmission estimation modeling to evaluate PS configurations in multi-band systems with optimized launch power distributions. We present results of network planning studies for C-band systems in a national and a continental optical backbone network topology considering different granularities of the configurations. Our analysis confirms that finer modulation-based rate-adaptivity results in substantial resource savings, decreasing the number of necessary lightpaths by at most 13% in C-band EONs. Additional savings are observed in multi-band systems, showing further increased savings in the number of required lightpaths of up to 20%. In contrast, increased symbol rate granularity only results in minor savings.
最近，为支持带宽密集型应用日益增长的需求，灵活网格弹性光网络（EON）得到了广泛部署。为了经济高效地扩展网络容量，多频段系统是一种很有前途的解决方案。需要优化 EON 的使用，以延迟成本密集型网络升级，降低成本和功耗。下一代带宽可变收发器（BVT）将通过概率整形（PS）等技术提高符号率和调制的适应性。在这项工作中，我们研究了配置粒度增加对光网络的影响。我们考虑了 BVT 配置的实际实施因素，以估算所需的信噪比。此外，我们还提出了一种优化算法，可为每个考虑的数据速率和带宽组合选择最有效的配置。我们利用先进的传输质量估算建模来评估具有优化发射功率分布的多频段系统中的 PS 配置。我们介绍了在国家和大陆光骨干网拓扑中对 C 波段系统进行网络规划研究的结果，其中考虑到了配置的不同粒度。我们的分析证实，更精细的基于调制的速率自适应可节省大量资源，在 C 波段 EON 中，必要光路的数量最多可减少 13%。在多波段系统中还能观察到更多的节省，显示所需光路的数量进一步增加，最多可节省 20%。相比之下，提高符号率粒度只能节省少量费用。
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