Adaptive and Learning-Driven Resource Optimization Framework for Scalable Cloud Computing Systems
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With the rapid development of cloud computing technologies in 2024, large-scale distributed systems face increasing challenges in resource allocation, latency control, and energy efficiency. This paper proposes a scalable and adaptive resource optimization framework that integrates deep learning-based prediction models with dynamic scheduling strategies. By leveraging workload forecasting and real-time feedback mechanisms, the proposed framework improves resource utilization and system stability. Experimental results demonstrate that the framework achieves significant improvements in latency reduction and energy efficiency compared with conventional approaches. The proposed method provides a robust solution for modern cloud environments characterized by high variability and heterogeneous workloads.
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