IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction
IT equipment is the largest electricity consumer in data centers. However, existing IT equipment energy consumption prediction methods can only capture temporal dependencies between features and cannot uncover spatial dependencies between features. Furthermore, these methods cannot dynamically pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Chinese |
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Harbin University of Science and Technology Publications
2024-10-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2367 |
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| _version_ | 1839636372423442432 |
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| author | CHEN Xiaojiang HUANG Hongcong CAI Xuelong DING Bo |
| author_facet | CHEN Xiaojiang HUANG Hongcong CAI Xuelong DING Bo |
| author_sort | CHEN Xiaojiang |
| collection | DOAJ |
| description |
IT equipment is the largest electricity consumer in data centers. However, existing IT equipment energy consumption prediction methods can only capture temporal dependencies between features and cannot uncover spatial dependencies between features. Furthermore, these methods cannot dynamically predict energy consumption based on the type of task, which leads to inaccurate predictions. To address these problems, this paper proposes an IT equipment energy consumption prediction method based on Long Short-Term Memory Network (LSTM) and Graph Convolutional Neural Network (GCN). In this method, the LSTM is first used to capture the temporal dependencies of IT equipment energy consumption features. And then a graph structure is constructed and the spatial dependencies between features are uncovered through GCN, and periodically captures the dynamic energy consumption patterns of IT equipment. Next, an attention module is used to weight the features for different importance levels, and the final energy consumption prediction is obtained. Experimental results show that the proposed energy consumption prediction method achieves MAPE of 1. 48% and RMSE of 1. 55 , which are much better than other existing methods. IT equipment can be configured and scheduled based on energy consumption prediction results, which can achieve energy saving and emission reduction in the data center. |
| format | Article |
| id | doaj-art-d3827a30a60e4b8faecdfcb22367000d |
| institution | Matheson Library |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-d3827a30a60e4b8faecdfcb22367000d2025-07-08T00:41:45ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-10-012905566410.15938/j.jhust.2024.05.007IT Equipment Automatic Configuration Method Based on Energy Consumption PredictionCHEN Xiaojiang0HUANG Hongcong1CAI Xuelong2DING Bo3China Southern Power Grid Digital Grid Group Information and Telecommunication Technology Co. , Ltd. , Guangzhou 510663 , ChinaChina Southern Power Grid Digital Grid Group Information and Telecommunication Technology Co. , Ltd. , Guangzhou 510663 , ChinaChina Southern Power Grid Digital Grid Group Information and Telecommunication Technology Co. , Ltd. , Guangzhou 510663 , ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080 , China IT equipment is the largest electricity consumer in data centers. However, existing IT equipment energy consumption prediction methods can only capture temporal dependencies between features and cannot uncover spatial dependencies between features. Furthermore, these methods cannot dynamically predict energy consumption based on the type of task, which leads to inaccurate predictions. To address these problems, this paper proposes an IT equipment energy consumption prediction method based on Long Short-Term Memory Network (LSTM) and Graph Convolutional Neural Network (GCN). In this method, the LSTM is first used to capture the temporal dependencies of IT equipment energy consumption features. And then a graph structure is constructed and the spatial dependencies between features are uncovered through GCN, and periodically captures the dynamic energy consumption patterns of IT equipment. Next, an attention module is used to weight the features for different importance levels, and the final energy consumption prediction is obtained. Experimental results show that the proposed energy consumption prediction method achieves MAPE of 1. 48% and RMSE of 1. 55 , which are much better than other existing methods. IT equipment can be configured and scheduled based on energy consumption prediction results, which can achieve energy saving and emission reduction in the data center.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2367energy consumption predictionit equipment automatic configurationlong short-term memory networkgraph convolutional network |
| spellingShingle | CHEN Xiaojiang HUANG Hongcong CAI Xuelong DING Bo IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction Journal of Harbin University of Science and Technology energy consumption prediction it equipment automatic configuration long short-term memory network graph convolutional network |
| title | IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction |
| title_full | IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction |
| title_fullStr | IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction |
| title_full_unstemmed | IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction |
| title_short | IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction |
| title_sort | it equipment automatic configuration method based on energy consumption prediction |
| topic | energy consumption prediction it equipment automatic configuration long short-term memory network graph convolutional network |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2367 |
| work_keys_str_mv | AT chenxiaojiang itequipmentautomaticconfigurationmethodbasedonenergyconsumptionprediction AT huanghongcong itequipmentautomaticconfigurationmethodbasedonenergyconsumptionprediction AT caixuelong itequipmentautomaticconfigurationmethodbasedonenergyconsumptionprediction AT dingbo itequipmentautomaticconfigurationmethodbasedonenergyconsumptionprediction |