Resource Optimization of SFC Embedding for IoT Networks Using Quantum Computing

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.