Resource Optimization of SFC Embedding for IoT Networks Using Quantum Computing

Mahzabeen Emu, Salimur Choudhury, K. Salomaa
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引用次数: 1

Abstract

Embedding Service Function Chain (SFC) into the massive and resource-hungry Internet of Things (IoT) substrate graph is a critical optimization research problem. Unfortunately, the classical Integer Linear Programming (ILP) formulation for such problems is usually NP-hard. Thus, this research study presses on the need to go beyond the realms and employ Quantum Annealing (QA) to speed up the computation. To comply, we reformulate the SFC embedding problem into IoT graphs as Quadratic Unconstrained Binary Optimization (QUBO) format and propose a hybrid warm start quantum annealing (WSQA) optimization technique. Simulation results show that our proposed WSQA can improve resource utilization, accelerate computing time, and achieve a better scalability success rate at solving large-scale SFC deployment compared to standalone QA. Further along the line, this research inspires the application of quantum optimization for resource allocation in next-generation networks even with the limited availability of qubits.
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基于量子计算的物联网SFC嵌入资源优化
将业务功能链(SFC)嵌入海量资源密集型的物联网(IoT)基板图中是一个关键的优化研究问题。不幸的是,这类问题的经典整数线性规划(ILP)公式通常是np困难的。因此,本研究迫切需要超越这个领域,采用量子退火(QA)来加快计算速度。为此,我们将SFC嵌入问题重新表述为二次无约束二进制优化(QUBO)格式,并提出了一种混合热启动量子退火(WSQA)优化技术。仿真结果表明,与独立QA相比,我们提出的WSQA可以提高资源利用率,加快计算时间,并在解决大规模SFC部署时获得更高的可扩展性成功率。进一步地,这项研究激发了量子优化在下一代网络中资源分配的应用,即使量子比特的可用性有限。
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