With the development of quantum computing and computer science, the risk of traditional cryptographic algorithms being cracked is increasing. Therefore, quantum key distribution (QKD) technology based on quantum physical properties has been developed to ensure the security of information communication. Large-scale QKD networks can be built using trusted relay technology, but the hops encryption and decryption process of trusted relay will consume extra keys, which makes it particularly important to choose a reasonable path to reduce the consumption of extra keys during key distribution. The existing routing algorithms mainly focus on the number of remaining keys and hops, but ignore the congestion information of the network, resulting in a high key exchange failure rate and an increase in key consumption. In view of this background, we propose a load balancing routing (LBR) algorithm, which can realize congestion control in QKD network by dynamically detecting link state with SDN controller. Simulation results show that LBR improves key request success rate by more than 10% and key utilization efficiency by about 5% compared with existing methods.
With the development of quantum computing and computer science, the risk of traditional cryptographic algorithms being cracked is increasing. Therefore, quantum key distribution (QKD) technology based on quantum physical properties has been developed to ensure the security of information communication. Large-scale QKD networks can be built using trusted relay technology, but the hops encryption and decryption process of trusted relay will consume extra keys, which makes it particularly important to choose a reasonable path to reduce the consumption of extra keys during key distribution. The existing routing algorithms mainly focus on the number of remaining keys and hops, but ignore the congestion information of the network, resulting in a high key exchange failure rate and an increase in key consumption. In view of this background, we propose a load balancing routing (LBR) algorithm, which can realize congestion control in QKD network by dynamically detecting link state with SDN controller. Simulation results show that LBR improves key request success rate by more than 10% and key utilization efficiency by about 5% compared with existing methods.
Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.
Since the QKD network can overcome the distance limitation and expand the point-to-point QKD system to a multi-user key distribution system, some testing QKD networks have been built. However, all of this previous research seldom focused on the routing mechanism of QKD network in detail. Therefore, this paper focuses on the routing issue in trust relaying QKD network, builds a model of the trust relaying QKD network and proposes a secret-key-aware routing method. In our method, a dynamic model for the residual local key is proposed to forecast the residual local key quantity of each QKD link more accurately, and the cost of QKD link and relaying path are defined by multiple affecting factors, e.g. the generation, consumption rate and the local key depletion index. The proposed method is implemented and evaluated in a simulation environment. The simulation results show that our routing method can increase the success rate of key exchange, make all the QKD links participate key exchange with almost equal opportunity to achieve load balance, and trade off the local key generation and consumption of each QKD link. Therefore, our proposed method can contribute to effectively improve the holistic performance of the trust relaying QKD network.
With quantum computing advancing, traditional encryption risks cracking, making quantum key distribution (QKD) crucial for its quantum-physics-based unconditional security. Large-scale QKD networks face high key consumption, uneven allocation, and reduced long-distance generation rates. This paper proposes MTSA, a time slot allocation-based multi-path routing scheme using linear programming and SDN for load balancing, with a two-level cache queue and optimized polling algorithm. Simulations on NSFNET/USNET show MTSA boosts key utilization by 15.565% and distribution success rate by 12.534%, optimizing QKD network performance.
With the continuous development of quantum computing technology, Quantum Key Distribution (QKD) is an important means of secure communication in the post-quantum era. However, the high cost of trusted routing relays in QKD networks has hindered its widespread adoption in power systems. Reducing costs is the key to the popularization of QKD in large-scale data grid scenarios. Therefore, this paper proposes a routing scheme for QKD network in a hybrid trusted environment. Our scheme is based on SDN architecture, utilizing the characteristics of QKD network in hybrid trusted environment, through prioritizing the selection of trusted paths (composed entirely of trusted quantum nodes) and backup selection of hybrid trusted paths (composed of both trusted and semi-trusted quantum nodes), implementing a unified dynamic routing strategy to obtain the optimal quantum routing path. Furthermore, by collecting topology information and real-time link status of the power grid, a calculation method for the number of paths and link costs is proposed to maximize load balancing in the network. Finally, the simulation results demonstrate that our scheme outperforms routing schemes relying solely on trusted quantum nodes for key distribution, as well as other cooperative routing schemes involving semi-trusted quantum nodes, in terms of success rate of distribution and load balancing.
Quantum Key Distribution (QKD) is a technique for sharing encryption keys between two adjacent nodes in an unconditionally secure manner based on quantum theory. From the viewpoint of network system research, QKD is considered to be a building block for providing secure link communication in network systems. Although the communication distance is subject to a limitation attributable to the QKD fundamentals, recent research and some QKD field trials of “key relaying” over a “QKD network” are overcoming this limitation. It also enables each node on the QKD network to exchange encryption keys with arbitrary nodes on the QKD network. However, the previous research didn't address a routing method on the QKD network in detail. This paper focuses on the routing issues on the QKD network, clarifies the requirements of the routing protocol, and proposes a routing solution designed for the key relaying on the QKD network. The proposed routing solution consists of 4 components: (1) a node interface architecture addressing both encrypted and unencrypted traffics, (2) a path selection algorithm based on the number of keys, (3) an IP address assignment scheme connecting unencrypted interfaces and encrypted interfaces, and (4) a routing protocol deployment method consuming no extra keys. The proposed routing solution allows the QKD network to select a suitable key relaying path according to the QKD system condition and to reduce the extra key consumption, which optimizes the key utilization on the QKD network. The proposed routing solution was implemented with a network emulating QKD technology and evaluated. The evaluation result shows that the proposed routing solution could select a path that stores enough keys, which is suitable for a QKD network. Additional impact analysis reveals that the proposed routing solution could save up to 52% of key consumption depending on the QKD key sharing speed and the routing protocol configuration.
Efficient and scalable quantum key scheduling remains a critical challenge in trusted-relay Quantum Key Distribution (QKD) networks due to imbalanced key resource utilization, dynamic key consumption, and topology-induced congestion. This paper presents a Q-learning-based adaptive routing framework designed to optimize quantum key delivery in dynamic QKD networks. The model formulates routing as a Markov Decision Process, with a compact state representation that combines the current node, destination node, and discretized key occupancy levels. Reward function is designed to jointly penalize resource imbalance and rapid key depletion while promoting traversal through links with sustainable key generation, guiding the agent toward balanced and congestion-aware decisions. Compared to Dijkstra, the Q-learning scheduler achieves significantly lower delivery latency, more effective key resource utilization, and greater reliability: maintaining 45.8 vs 118.5 hops in average delay under heavy load, sustaining 74.5\% vs 61.5\% in average key utilization, and reducing the failure ratio to 7.2\% vs 41.5\%, collectively confirming its advantages in scalability, congestion resilience, and resource-efficient decision making.