Quantum key distribution (QKD) can provide long-term security for numerous users. Currently, quantum networks are still in the early stages of small-scale deployment, most of which can only support a single QKD protocol (QKDP). However, with the advancement of various QKDPs, a single-protocol quantum network is no longer sufficient to meet the demands of multiple users, prompting the emergence of multi-protocol quantum networks. Nevertheless, the transition from a single-protocol to a multi-protocol quantum network still faces many challenging issues, such as key negotiation interruptions due to device initialization and channel calibration during protocol updating. To address the quantum key negotiation interruption problem, we propose a seamless key negotiation oriented multi-protocol updating algorithm in this work, which can fulfill the protocol updating requests of different users in quantum metropolitan networks. Furthermore, to better improve network performance while meeting diverse user demands, we propose four heuristic algorithms for optimal QKDP recommendation, focusing on their applications for multi-protocol updating in different types of typical networks. We perform the simulations with different QKDP recommendation algorithms to analyze the impact of the cache time of the key cache area on the key negotiation interruption probability and the time resource utilization. Simulation results demonstrate that the proposed algorithm can reduce the key negotiation interruption probability by 77.7% while increasing the time resource utilization by 15.3% compared to no key cache area.
{"title":"Multi-protocol updating for seamless key negotiation in quantum metropolitan networks","authors":"Jiali Zhu;Yuan Cao;Mingxuan Guo;Xingyu Zhou;Chunhui Zhang;Jian Li;Xiaosong Yu;Yongli Zhao;Jie Zhang;Qin Wang","doi":"10.1364/JOCN.511999","DOIUrl":"https://doi.org/10.1364/JOCN.511999","url":null,"abstract":"Quantum key distribution (QKD) can provide long-term security for numerous users. Currently, quantum networks are still in the early stages of small-scale deployment, most of which can only support a single QKD protocol (QKDP). However, with the advancement of various QKDPs, a single-protocol quantum network is no longer sufficient to meet the demands of multiple users, prompting the emergence of multi-protocol quantum networks. Nevertheless, the transition from a single-protocol to a multi-protocol quantum network still faces many challenging issues, such as key negotiation interruptions due to device initialization and channel calibration during protocol updating. To address the quantum key negotiation interruption problem, we propose a seamless key negotiation oriented multi-protocol updating algorithm in this work, which can fulfill the protocol updating requests of different users in quantum metropolitan networks. Furthermore, to better improve network performance while meeting diverse user demands, we propose four heuristic algorithms for optimal QKDP recommendation, focusing on their applications for multi-protocol updating in different types of typical networks. We perform the simulations with different QKDP recommendation algorithms to analyze the impact of the cache time of the key cache area on the key negotiation interruption probability and the time resource utilization. Simulation results demonstrate that the proposed algorithm can reduce the key negotiation interruption probability by 77.7% while increasing the time resource utilization by 15.3% compared to no key cache area.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 7","pages":"735-749"},"PeriodicalIF":5.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141333960","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}
Today, IP-Optical networks apply IP restoration as the default strategy to recover IP traffic from optical failures. This strategy has been preferred over optical restoration as it circumvents the lengthy delays involved in the reconfiguration of the optical layer. Although fast, IP restoration requires the overprovisioning of costly capacity to cope with optical failures. The advent of software-defined optical networking enables a changeover towards more efficient methods that integrate IP-Optical restoration. These methods should not only restore traffic from failures considered in the planning phase, but they should also efficiently restore traffic from unforeseen failures. This paper studies this problem by investigating optimization algorithms for capacity planning and multilayer restoration based on the theory of adjustable robust optimization (ARO). The approach performs offline optimization of the capacities of IP links as well as the routing and capacities of IP tunnels in both failure-free mode of operation and in a foreseen set of optical failures. Besides, the approach optimizes an affine policy that is applied online to recover IP traffic from unforeseen failures, thereby providing robustness to optical failures not regarded in the planning phase. To overcome the limitations of the affine policy, an alternative robust algorithm is formulated based on deep reinforcement learning (DRL) and graph neural networks (GNNs). By training a DRL-GNN agent, the performance of the restoration process is improved by further minimizing the traffic losses when unforeseen optical failures occur. Results in selected scenarios show that the algorithms outperform IP restoration in terms of capacity requirements, while minimizing the traffic losses in the case of failures. Moreover, the DRL-GNN method significantly improves the ARO-based affine algorithm, which shows the capability of learning the complex relationship between the capacity impairments caused by optical failures and the routing strategy required to restore IP traffic.
如今,IP-光网络将 IP 恢复作为从光故障中恢复 IP 流量的默认策略。与光恢复相比,这种策略更受青睐,因为它避免了光层重新配置所带来的长时间延迟。IP 恢复虽然速度快,但需要超额配置昂贵的容量来应对光故障。软件定义光网络的出现,使我们能够转向集成 IP 光恢复功能的更高效方法。这些方法不仅能从规划阶段考虑的故障中恢复流量,还能从不可预见的故障中有效恢复流量。本文以可调整鲁棒优化(ARO)理论为基础,通过研究容量规划和多层恢复的优化算法来研究这一问题。该方法在无故障运行模式和预知光故障情况下,对 IP 链路的容量以及 IP 隧道的路由和容量进行离线优化。此外,该方法还优化了仿射策略,该策略可在线应用,以从不可预见的故障中恢复 IP 流量,从而为规划阶段未考虑到的光故障提供稳健性。为了克服仿射策略的局限性,基于深度强化学习(DRL)和图神经网络(GNN)制定了另一种稳健算法。通过训练 DRL-GNN 代理,可在发生不可预见的光故障时进一步将流量损失降至最低,从而提高恢复过程的性能。选定场景中的结果表明,这些算法在容量要求方面优于 IP 恢复,同时在故障情况下最大限度地减少了流量损失。此外,DRL-GNN 方法显著改进了基于 ARO 的仿射算法,显示了学习光故障造成的容量损害与恢复 IP 流量所需的路由策略之间复杂关系的能力。
{"title":"Multilayer restoration in IP-Optical networks by adjustable robust optimization and deep reinforcement learning","authors":"Malek Bekri;Ronald Romero Reyes;Thomas Bauschert","doi":"10.1364/JOCN.523894","DOIUrl":"10.1364/JOCN.523894","url":null,"abstract":"Today, IP-Optical networks apply IP restoration as the default strategy to recover IP traffic from optical failures. This strategy has been preferred over optical restoration as it circumvents the lengthy delays involved in the reconfiguration of the optical layer. Although fast, IP restoration requires the overprovisioning of costly capacity to cope with optical failures. The advent of software-defined optical networking enables a changeover towards more efficient methods that integrate IP-Optical restoration. These methods should not only restore traffic from failures considered in the planning phase, but they should also efficiently restore traffic from unforeseen failures. This paper studies this problem by investigating optimization algorithms for capacity planning and multilayer restoration based on the theory of adjustable robust optimization (ARO). The approach performs offline optimization of the capacities of IP links as well as the routing and capacities of IP tunnels in both failure-free mode of operation and in a foreseen set of optical failures. Besides, the approach optimizes an affine policy that is applied online to recover IP traffic from unforeseen failures, thereby providing robustness to optical failures not regarded in the planning phase. To overcome the limitations of the affine policy, an alternative robust algorithm is formulated based on deep reinforcement learning (DRL) and graph neural networks (GNNs). By training a DRL-GNN agent, the performance of the restoration process is improved by further minimizing the traffic losses when unforeseen optical failures occur. Results in selected scenarios show that the algorithms outperform IP restoration in terms of capacity requirements, while minimizing the traffic losses in the case of failures. Moreover, the DRL-GNN method significantly improves the ARO-based affine algorithm, which shows the capability of learning the complex relationship between the capacity impairments caused by optical failures and the routing strategy required to restore IP traffic.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"16 7","pages":"721-734"},"PeriodicalIF":5.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099619","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}
We propose and demonstrate a novel O-band wavelength division multiplexing (WDM) optical transceiver enabled by the hybrid photonic integration of indium phosphide (InP) components into a polymer-based photonic motherboard called PolyBoard. The optical engine hosts an eight-fold InP electro-absorption modulated laser (EML) array at the transmitter part exhibiting ${gt}{{35}};{rm{GHz}}$