Juan Carlos Hernandez-Hernandez , David Larrabeiti , Maria Calderon , Ignacio Soto , Bruno Cimoli , Hui Liu , Idelfonso Tafur Monroy
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引用次数: 0
Abstract
Time-sharing of Quantum Key Distribution (QKD) transceivers with the help of optical switches and a central Software-Defined Networking (SDN) controller is a promising technique to better amortize the large investments required to build a Quantum Key Distribution Network (QKDN). In this work, we investigate the implications of introducing Time-Division Multiplexing (TDM) in trusted-relay QKDNs at the wide-area network scale in terms of performance and cost-saving. To this end, we developed both a Mixed Integer Linear Programming (qTDM-MILP) model and a Heuristic Algorithm (qTDM-HA) to solve the allocation of QKD transceivers and network resources for a novel switched QKDN operating scheme: qTDM-QKDN. Our heuristic method provides a close-to-optimal resource planning for the offline problem that computes the minimum number of QKD transceivers and optical switch ports at each node, as well as the number of quantum channels on each link required to satisfy a target set of end-to-end secret-keyrate demands. Moreover, both the model and the heuristic provide the time fractions that each QKD transceiver needs to peer with each neighbor QKD transceiver. We compared our proposed model and heuristic algorithm for cost minimization with non-time sharing QKD transceivers (nTDM) as baseline. The results show that qTDM can achieve substantial cost-savings in the range of 10%–40% compared to nTDM. Furthermore, this work sheds light on the selection of the value for the working cycle and its influence on network performance.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.