GreenBytes: Intelligent Energy Estimation for Edge-Cloud
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909449248047104 |
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| author | Kassai, Kasra Dagiuklas, Tasos Bashir, Satwat Iqbal, Muddesar |
| author_facet | Kassai, Kasra Dagiuklas, Tasos Bashir, Satwat Iqbal, Muddesar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GreenBytes: Intelligent Energy Estimation for Edge-Cloud Kassai, Kasra Dagiuklas, Tasos Bashir, Satwat Iqbal, Muddesar Distributed, Parallel, and Cluster Computing Emerging Technologies Networking and Internet Architecture 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. |
| title | GreenBytes: Intelligent Energy Estimation for Edge-Cloud |
| topic | Distributed, Parallel, and Cluster Computing Emerging Technologies Networking and Internet Architecture |
| url | https://arxiv.org/abs/2403.04665 |