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QoS-aware quantum service composition using multi-objective optimization
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AI
DOI：10.1007/s42484-026-00366-9.png)
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Abstract
En 中文
Cloud service composition involves selecting and orchestrating multiple interdependent services to fulfil complex user requirements. Traditional service composition methods struggle to efficiently handle multiple conflicting QoS factors such as latency, cost, reliability, and availability. This paper proposes a novel quantum-assisted service composition framework that leverages Quantum Approximate Optimization Algorithm (QAOA) to solve the multi-objective optimization problem inherent in composite cloud services. The proposed system models service composition as a Quadratic Unconstrained Binary Optimization (QUBO) problem, where each quantum bit (qubit) represents a service selection decision under specific QoS constraints. A hybrid quantum-classical approach integrates classical pre-processing with QAOA-based optimization on a quantum simulator to identify optimal service chains. Experimental evaluations conducted on benchmark service datasets demonstrate that our method achieves near-optimal solutions with reduced time complexity compared to classical heuristics such as NSGA-II and ACO. The results show that quantum computing can effectively address complex service orchestration challenges in future cloud environments.
Keywords:
Quantum multi-objective optimization
Service chaining
QoS-aware composition
Journal
Q
IF:
4.4
Papers:
427
Citations:
796