Virtualizing Quantum Key Distribution Based on Network Function Virtualization
Network functions virtualization (NFV) refers to replacing actual network appliances with virtual machines. A virtual machine monitor (VMM) has been used effectively in operating networking software and routing and load-balancing procedures. It is software that allows one computer to run multiple vi...
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Published in: | 2023 3rd International Scientific Conference of Engineering Sciences (ISCES) pp. 48 - 53 |
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Main Authors: | , |
Format: | Conference Proceeding |
Language: | English |
Published: |
IEEE
03-05-2023
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Subjects: | |
Online Access: | Get full text |
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Summary: | Network functions virtualization (NFV) refers to replacing actual network appliances with virtual machines. A virtual machine monitor (VMM) has been used effectively in operating networking software and routing and load-balancing procedures. It is software that allows one computer to run multiple virtual machines (VMs) by sharing its resources virtually. Quantum key distribution (QKD) is a novel security technology that utilizes quantum mechanical principles to perform its functions. These functions include the exchange of a secret key and the provision of absolute protection. One of the unique capabilities QKD possesses, which is not found in other encryption techniques, is the ability to identify the existence of an eavesdropper. To accommodate the ever-increasing demand, service providers must increase their network infrastructure investments. In this study, we create a NFV system to generate a virtual quantum key distribution relay with virtual machines (VMs) and Amazon Web Services (AWS). The results of comparing quantum key distribution in a virtual environment and a traditional environment are as follows: the gain time in the virtual environment was approximately 0.0156 per second, with valid keys, while the traditional environment had a minimum time of 0.0312 per second, and the keys were invalid. The quantum bit error rate (QBER) is lower in NFV than in traditional methods. |
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DOI: | 10.1109/ISCES58193.2023.10311483 |