Quantum- Assisted Optimization Techniques for Large-scale Cloud Resource Scheduling

Abstract

Efficient resource scheduling in large-scale cloud environments remains a critical challenge due to dynamic workloads, heterogeneous resources, and scalability constraints. Classical optimization techniques often face limitations in handling the combinatorial complexity of such systems. This paper explores quantum-assisted optimization techniques for cloud resource scheduling, leveraging hybrid quantum-classical approaches to enhance solution quality and convergence efficiency. The proposed framework integrates quantum-inspired algorithms with cloud orchestration mechanisms to improve task allocation, reduce scheduling latency, and optimize resource utilization. Experimental analysis demonstrates that the proposed approach achieves better performance compared to conventional scheduling methods, particularly under high-demand and large-scale scenarios. These f indings highlight the potential of quantum-assisted methods as a promising direction for next-generation cloud resource management.