GreenBytes: Intelligent Energy Estimation for Edge-Cloud
CoRR(2024)
摘要
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.
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