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（LSTM）网络与梯度提升（Gradient Booster）模型——在Kubernetes集群环境中用于精确能耗估算的应用。其目标是通过对不同计算节点的能源使用提供精准预测，以提升可持续计算实践水平。通过对主节点与工作节点上模型性能的细致分析，本研究揭示了这些模型在提升能效方面的优势与潜在应用场景。LSTM模型展现出卓越的预测准确性，尤其在捕捉随时间变化的动态计算负载方面表现突出，其低均方误差（MSE）值以及紧密跟踪实际能耗趋势的能力证实了这一点。相比之下，梯度提升模型虽具有略高的MSE值，但在不同计算环境中均表现出优异的鲁棒性与适应能力。本研究强调了这些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行中的环境可持续性。