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IEEE Transactions on Network and Service Management

Multi-Tenant Provisioning for Quantum Key Distribution Networks With Heuristics and Reinforcement Learning: A Comparative Study

Yuan Cao, Yongli Zhao, Jun Li, Rui Lin, Jie Zhang, Jiajia Chen

Published Jan 7, 2020• 53 citations
Publisher

Abstract

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.

DOI
10.1109/tnsm.2020.2964003

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Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.
Quantum key distribution (QKD) networks are promising to progress towards widespread practical deployment over existing fiber infrastructures in the near future. Given the high cost and difficulty of deploying QKD networks, multi-tenancy becomes promising to improve cost efficiency for future QKD networks. In a multi-tenant QKD network, multiple QKD tenants can share the same QKD network infrastructure to obtain secret keys for securing their data transfer. Since the secret-key resources are finite and precious in QKD networks, how to achieve efficient multi-tenant secret-key assignment (MTKA) to satisfy the secret-key demands of multiple QKD tenants over QKD networks becomes a significant problem. In this regard, this study addresses the MTKA problem over QKD networks. A new multi-tenant QKD network architecture is proposed based on software defined networking (SDN) and quantum key pool (QKP) techniques. A secret-key rate sharing scheme is presented and a heuristic algorithm is designed to implement efficient MTKA over QKD networks. A new performance metric, namely matching degree (MD) that reflects the balance between QKD network secret-key resources and QKD tenant requests, is defined and evaluated. Simulation studies indicate that high QKD tenant requests accommodation and efficient secret-key resource usage can be achieved via maximizing the value of MD.
Quantum key distribution (QKD) has potential to provide long-term security for communications across the Internet. As security-hungry applications dramatically rise nowadays, a growing number of QKD networks are promising to be deployed in the immediate future. Nevertheless, a high-security-demand institution (e.g., a bank) needs to pay a high price to deploy its dedicated QKD network, while a cost-effective way of overcoming this challenge is to make multiple tenants share a QKD network. Each tenant represents a high-security-demand institution and can obtain secret keys on demand from the QKD network infrastructure for security purposes. Hence, how to achieve efficient and flexible multi-tenant provisioning over a QKD network becomes a crucial problem. This work introduces software defined networking (SDN) to address this problem. We experimentally demonstrate multi-tenant provisioning (including tenant establishment, adjustment, and deletion) over SDN-enabled metropolitan area QKD networks. A SDN-enabled metropolitan area QKD network architecture is introduced. We present a workflow, protocol extensions, and an on-demand secret-key resource allocation strategy for multi-tenant provisioning, which are all demonstrated by establishing an experimental testbed in the lab. Experimental results verify the effectiveness and flexibility of our SDN-based approaches for multi-tenant provisioning over metropolitan area QKD networks. Moreover, we conduct the simulation and discover the ways of improving the success probability of multi-tenant provisioning over metropolitan area QKD networks.
With its information-theoretic security, quantum-key-distribution-enabled optical networks (QKD-ON) have become a promising candidate for future optical networks. The concept of quantum key pool (QKP) was introduced to offer an effective strategy for storing quantum keys. However, with the loss on its theoretical security due to storing these keys, balancing the storage of quantum keys and the security requirements of QKD-ONs poses a major challenge in their practical deployments. Hence, in this paper a concept of quasi-real-time key provisioning (QRT-KP) is introduced to address the tradeoff between quantum key storage and the degree of security. To satisfy the practical deployment of QRT-KP and the requirement of high-traffic flow, we propose a multi-path based QRT-KP (MP-QRT-KP) algorithm. Simulation results show that the MP-QRT-KP effectively enhances the performance of QKD-ONs in different scenarios, and it turns out that the algorithm performs better than single-path based QRT-KP (SP-QRT-KP) in terms of the success probability of key-allocation requests and key-resources utilization.
Quantum Key Distribution (QKD) networks are proposed as a solution to ensure information-theoretic security for symmetric key exchange in post-quantum networks. Key generation is limited and variable, hence Quantum Key Pools (QKPs) have been proposed to allow applications a quicker access to quantum keys for secure data exchange. Nevertheless, On-Demand (OD) key relaying can still entail a delay too large for some applications. Therefore, the concept of key provisioning has been proposed to expedite key supply whenever necessary, resulting in Virtual QKPs for non-adjacent QKD nodes. After the implementation of a key pre-provisioning strategy inspired by an approach proposed by other authors, some novel variants are proposed that aim to decrease demand blocking due to the lack of keys, without significantly increasing the delay for key supply. In this work, an experimental study is conducted, where different settings are considered for the simulation parameters (including the QKP capacity). We conclude that the different pre-provisioning strategies allow for a reduction in the blocking probability (calculated for demands and for data) and in the delay, when compared to an OD strategy.
Abstract Quantum key distribution (QKD) optical networks, relying on the information-theoretic security guaranteed by the fundamental laws of quantum physics, have become a core solution for ensuring data transmission security in critical fields such as finance and energy. However, QKD optical networks have inherent characteristics like severe resource constraints, dynamic changes in network environments. The collaborative optimization of routing, wavelength, and time-slot assignment (RWTA) has thus become a key bottleneck restricting their practical deployment. Traditional algorithms suffer from high complexity and poor adaptability in large-scale dynamic networks, while existing deep reinforcement learning (DRL) schemes insufficiently consider network security states, failing multi-objective co-optimization. Therefore, this paper proposes a multi-objective secure proximal policy optimization algorithm (MOS-PPO) based on DRL to solve the routing, wavelength, and time slots assignment problem in QKD optical networks. The MOS-PPO algorithm expands the state space by introducing a network security state vector that includes quantum bit error rate, risk coefficient, node capacity, and processing capability. It designs a multi-objective reward function, and establishes a security constraint mechanism to achieve the collaborative optimization of blocking probability (BP), resource utilization (RU), and security performance. Simulations on NSFNET and UBN24 topologies show MOS-PPO outperforms DRL-PPO algorithm, Security-aware Load balancing, first fit algorithm, and random fit algorithm. On NSFNET, average BP drops by 20.4%, 22.1%,28.4%, 36.7% and RU rises by 4.0%,3.9%, 22.2%, 31.0%, on UBN24, BP decreases by 9.3%, 11.4%, 18.8%, and 27.4% and RU increases by 3.8%, 21.2%, 10.3%, and 29.5%, with higher stable security margins. This study verifies the high adaptability and multi-objective optimization ability of the MOS-PPO algorithm in dynamic network environments, providing an efficient RWTA solution for the practical deployment of QKD optical networks.