학술논문

Resource Allocation in Quantum-Key-Distribution- Secured Datacenter Networks With Cloud–Edge Collaboration
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 10(12):10916-10932 Jun, 2023
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Cloud computing
Cryptography
Collaboration
Security
Resource management
Quantum computing
Heuristic algorithms
Caching
cloud–edge collaboration
communication
computation
cryptographic
datacenter networks (DCNs)
quantum key distribution (QKD)
resource allocation
Language
ISSN
2327-4662
2372-2541
Abstract
Datacenter networks (DCNs) with cloud–edge collaboration are emerging to satisfy the communication, computation, and caching (3C) requirements of future services such as cloud-based IoT services. However, the enroute data over DCNs with cloud–edge collaboration is likely to suffer from cyberattacks such as eavesdropping. A large number of services require not only 3C resources, but also cryptographic resources for encryption to ensure high security. Quantum key distribution (QKD) is a practical approach to provide secret keys for remote users with information-theoretic security against attacks from quantum computing. A QKD-secured DCN (QKD-DCN) with cloud–edge collaboration can be deployed to satisfy the communication, computation, caching, and cryptographic (4C) requirements of services. This article innovatively solves the new 4C resource-allocation (4CRA) problem in the network to minimize the cryptographic resource consumption. It formulates an integer linear programming (ILP) model and proposes a heuristic cryptographic-dependent 4CRA algorithm to find optimal solutions. The proposed algorithm is compared with two baseline 4CRA algorithms which, respectively, consider the minimized service delivery latency and the first-fit resource availability. Analytical simulations show that the proposed algorithm minimizes the key-resource-consumption ratio and the average key-resource consumption under static and dynamic traffic scenarios in different network topologies.