This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and potential applications of these models in promoting energy efficiency. The LSTM model demonstrates remarkable predictive accuracy, particularly in capturing dynamic computing workloads over time, evidenced by low mean squared error (MSE) rates and the ability to closely track actual energy consumption trends. Conversely, the Gradient Booster model showcases robustness and adaptability across different computational environments, despite slightly higher MSE values. The study underscores the complementary nature of these models in advancing sustainable computing practices, suggesting their integration into energy management systems could significantly enhance environmental sustainability in technology operations.

翻译：本研究探讨了先进机器学习模型——特别是长短期记忆网络（LSTM）与梯度提升模型——在Kubernetes集群环境中实现精准能耗估算的应用。旨在通过提供跨计算节点的精确能耗预测，推动可持续计算实践。通过对主节点与工作节点模型性能的细致分析，研究揭示了这些模型在促进能效提升方面的优势与潜在应用场景。LSTM模型展现出卓越的预测精度，尤其在捕捉动态计算工作负载的时间演化规律方面，其低均方误差（MSE）率与对实际能耗趋势的紧密追踪能力为其显著特征。相比之下，梯度提升模型虽MSE值略高，但展现出跨不同计算环境的鲁棒性与适应性。本研究强调了这两种模型在推进可持续计算实践中的互补特性，表明将其集成至能源管理系统可显著提升技术运营的环境可持续性。