Elastic optical networks (EONs) operating in the C-band have been widely deployed worldwide. However, two major technologies—multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs)—can significantly increase network capacity beyond traditional EONs. A one-time greenfield deployment of these flexible-grid technologies may not be practical, as existing investments in flexible-grid EONs need to be preserved and ongoing services must face minimal disruption. Therefore, we envision the coexistence of flexible-grid, multiband, and multicore technologies during the brownfield migration. Each technology represents a tradeoff between higher capacity and greater deployment overhead, directly impacting network performance. Moreover, as traffic demands continue rising, capacity exhaustion becomes inevitable. Considering the different characteristics of these technologies, we propose a robust network planning solution called Progressive Optics Deployment and Integration for Growing Yields (PRODIGY+) to gradually migrate current C-band EONs. PRODIGY+ employs proactive measures inspired by the Swiss Cheese Model, making the network robust to traffic peaks while meeting service level agreements. The upgrade strategy enables a gradual transition to minimize migration costs while continuously supporting increasing traffic demands. We provide a detailed comparison of our proposed PRODIGY+ strategy against baseline strategies, demonstrating its superior performance.
在 C 波段运行的弹性光网络(EON)已在全球广泛部署。然而,两种主要技术--多频带弹性光网络(MB-EON)和空分复用弹性光网络(SDM-EON)--可以显著提高网络容量,超越传统的 EON。一次性全新部署这些灵活光网络技术可能并不现实,因为需要保留对灵活光网络 EON 的现有投资,而且必须尽量减少对现有服务的干扰。因此,我们设想在棕地迁移过程中,灵活网格、多频段和多核技术将共存。每种技术都要在更高容量和更大部署开销之间进行权衡,从而直接影响网络性能。此外,随着流量需求的不断增长,容量耗尽将不可避免。考虑到这些技术的不同特性,我们提出了一种稳健的网络规划解决方案,称为 "渐进式光学部署和集成,促进产量增长"(PRODIGY+),以逐步迁移当前的 C 波段 EON。PRODIGY+ 采用受瑞士奶酪模型启发的前瞻性措施,使网络在满足服务水平协议的同时,还能应对流量高峰。升级策略可实现逐步过渡,最大限度地降低迁移成本,同时持续支持不断增长的流量需求。我们详细比较了我们提出的 PRODIGY+ 策略和基准策略,证明了其卓越的性能。
{"title":"PRODIGY+: a robust progressive upgrade approach for elastic optical networks","authors":"Shrinivas Petale;Aleksandra Knapinska;Egemen Erbayat;Piotr Lechowicz;Krzysztof Walkowiak;Shih-Chun Lin;Motoharu Matsuura;Hiroshi Hasegawa;Suresh Subramaniam","doi":"10.1364/JOCN.525392","DOIUrl":"10.1364/JOCN.525392","url":null,"abstract":"Elastic optical networks (EONs) operating in the C-band have been widely deployed worldwide. However, two major technologies—multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs)—can significantly increase network capacity beyond traditional EONs. A one-time greenfield deployment of these flexible-grid technologies may not be practical, as existing investments in flexible-grid EONs need to be preserved and ongoing services must face minimal disruption. Therefore, we envision the coexistence of flexible-grid, multiband, and multicore technologies during the brownfield migration. Each technology represents a tradeoff between higher capacity and greater deployment overhead, directly impacting network performance. Moreover, as traffic demands continue rising, capacity exhaustion becomes inevitable. Considering the different characteristics of these technologies, we propose a robust network planning solution called Progressive Optics Deployment and Integration for Growing Yields (PRODIGY+) to gradually migrate current C-band EONs. PRODIGY+ employs proactive measures inspired by the Swiss Cheese Model, making the network robust to traffic peaks while meeting service level agreements. The upgrade strategy enables a gradual transition to minimize migration costs while continuously supporting increasing traffic demands. We provide a detailed comparison of our proposed PRODIGY+ strategy against baseline strategies, demonstrating its superior performance.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 9","pages":"E48-E60"},"PeriodicalIF":4.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829895","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}
With progressive network recovery, operators restore network connectivity after massive failures along multiple stages, by identifying the optimal sequence of repair actions to maximize carried live traffic. Motivated by the initial deployments of quantum-key-distribution (QKD) over optical networks appearing in several locations worldwide, in this work we model and solve the progressive QKD network recovery (PQNR) problem in QKD networks to accelerate the recovery after failures. We formulate an integer linear programming (ILP) model to optimize the achievable accumulative key rates during recovery for four different QKD network architectures, considering different capabilities of using trusted relay and optical bypass. Due to the computational limitations of the ILP model, we propose a deep reinforcement learning (DRL) algorithm based on a twin delayed deep deterministic policy gradients (TD3) framework to solve the PQNR problem for large-scale topologies. Simulation results show that our proposed algorithm approaches well compared to the optimal solution and outperforms several baseline algorithms. Moreover, using optical bypass jointly with trusted relay can improve the performance in terms of the key rate by 14% and 18% compared to the cases where only optical bypass and only trusted relay are applied, respectively.
{"title":"DRL-based progressive recovery for quantum-key-distribution networks","authors":"Mengyao Li;Qiaolun Zhang;Alberto Gatto;Stefano Bregni;Giacomo Verticale;Massimo Tornatore","doi":"10.1364/JOCN.526014","DOIUrl":"10.1364/JOCN.526014","url":null,"abstract":"With progressive network recovery, operators restore network connectivity after massive failures along multiple stages, by identifying the optimal sequence of repair actions to maximize carried live traffic. Motivated by the initial deployments of quantum-key-distribution (QKD) over optical networks appearing in several locations worldwide, in this work we model and solve the progressive QKD network recovery (PQNR) problem in QKD networks to accelerate the recovery after failures. We formulate an integer linear programming (ILP) model to optimize the achievable accumulative key rates during recovery for four different QKD network architectures, considering different capabilities of using trusted relay and optical bypass. Due to the computational limitations of the ILP model, we propose a deep reinforcement learning (DRL) algorithm based on a twin delayed deep deterministic policy gradients (TD3) framework to solve the PQNR problem for large-scale topologies. Simulation results show that our proposed algorithm approaches well compared to the optimal solution and outperforms several baseline algorithms. Moreover, using optical bypass jointly with trusted relay can improve the performance in terms of the key rate by 14% and 18% compared to the cases where only optical bypass and only trusted relay are applied, respectively.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 9","pages":"E36-E47"},"PeriodicalIF":4.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641668","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}
There is a strong demand for creating primary/backup optical path pairs between a source and a destination node in order to continue a service when the primary path has some disorder. We also have to consider SRLG (shared risk link group)-disjoint primary/backup path settings for achieving robust path protection against possible network problems on primary paths. To deal with these issues, in this study, we use the $k$