Hybrid Quantum–Classical Optimization for Cloud Resource Allocation: A Scalable Framework for Energy–Efficient Computing

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ABSTRACT

Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.

Significance of the Study:

This study introduces a hybrid quantum-classical framework (QDO) for optimizing cloud resource allocation, addressing critical challenges in energy efficiency, cost reduction, and performance scalability. By integrating quantum annealing (QA) and variational quantum eigensolver (VQE) with classical methods, the QDO framework demonstrates significant improvements in resource utilization, operational costs, and SLA compliance. The findings highlight the transformative potential of quantum computing in cloud infrastructure, offering a scalable, sustainable solution for next-generation computing demands while paving the way for future advancements in hybrid optimization models.

Summary of the Study:

The study proposes a Quantum-Driven Optimization (QDO) framework combining quantum and classical techniques to enhance cloud resource allocation. Leveraging QA and VQE, QDO improves resource utilization by 27%, reduces costs by 34%, and decreases SLA violations by 18% compared to traditional methods. The hybrid approach ensures scalability and energy efficiency in dynamic cloud environments. While quantum hardware limitations remain, the framework sets a foundation for future research in multi-cloud integration and real-time quantum processing, advancing sustainable cloud computing solutions.