Quantum-Inspired AI Techniques for Optimizing Cloud Resource Allocation in Hybrid Architectures
DOI:
https://doi.org/10.64917/feaiml/Volume02Issue12-06Keywords:
quantum-inspired AI, cloud resource allocation, hybrid cloud scheduling, reinforcement learning, workload orchestration, Grover search, SLA optimization, cloud computing, edge-cloud systems, probabilistic schedulingAbstract
Hybrid cloud environments face persistent challenges in efficient resource allocation due to workload variability, SLA (Service Level Agreement) constraints, and the heterogeneity of infrastructure. This paper introduces a Quantum-Inspired AI (QIAI) model that leverages probabilistic scheduling logic, specifically simulating amplitude-guided exploration and tunneling-based state transitions, to enhance decision-making in complex scheduling scenarios. Implemented using classical hardware, the model is evaluated against traditional heuristics (Round-Robin, Least-Loaded First) and Q-Learning approaches using real-world workload traces from Google, Azure, and Alibaba. Experimental results demonstrate that the proposed model achieves a resource utilization rate exceeding 80% and reduces SLA violations to less than 6%, significantly outperforming the 18.5% violation rate of baseline heuristics. Furthermore, the model exhibits superior efficiency, achieving convergence 35–40% faster than standard reinforcement learning agents. The findings suggest that quantum-inspired models can offer practical and scalable advantages in modern cloud computing systems without the need for actual quantum hardware.
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