Optimization of the Energy-Saving Data Storage Algorithm for Differentiated Cloud Computing Tasks Optimization of the Energy-Saving Data Storage Algorithm

This study presents a novel energy-saving data storage algorithm designed to enhance data storage efficiency and reduce energy consumption in cloud computing environments. By intelligently discerning and categorizing various cloud computing tasks, the algorithm dynamically adapts data storage strategies, resulting in a targeted optimization methodology that is both devised and experimentally validated. The study findings demonstrate that the optimized model surpasses comparative models in accuracy, precision, recall, and F1-score, achieving peak values of 0.863, 0.812, 0.784, and 0.798, respectively, thereby affirming the efficacy of the optimized approach. In simulation experiments involving tasks with varying data volumes, the optimized model consistently exhibits lower latency compared to Attention-based Long Short-Term Memory Encoder-Decoder Network and Deep Reinforcement Learning Task Scheduling models. Furthermore, across tasks with differing data volumes, the optimized model maintains high throughput levels, with only marginal reductions in throughput as data volume increases, indicating sustained and stable performance. Consequently, this study is pertinent to cloud computing data storage and energy-saving optimization, offering valuable insights for future research and practical applications.