Deep Recurrent Model for Server Load and Performance Prediction in Data Center
Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM) units for server...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/8584252 |
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| _version_ | 1832566779142995968 |
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| author | Zheng Huang Jiajun Peng Huijuan Lian Jie Guo Weidong Qiu |
| author_facet | Zheng Huang Jiajun Peng Huijuan Lian Jie Guo Weidong Qiu |
| author_sort | Zheng Huang |
| collection | DOAJ |
| description | Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM) units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests), which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center. |
| format | Article |
| id | doaj-art-ac6592920f4b4ac1a539754cd4506bfb |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ac6592920f4b4ac1a539754cd4506bfb2025-02-03T01:03:19ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/85842528584252Deep Recurrent Model for Server Load and Performance Prediction in Data CenterZheng Huang0Jiajun Peng1Huijuan Lian2Jie Guo3Weidong Qiu4School of Cyber Security, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Cyber Security, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Cyber Security, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Cyber Security, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Cyber Security, Shanghai Jiao Tong University, Shanghai, ChinaRecurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM) units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests), which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.http://dx.doi.org/10.1155/2017/8584252 |
| spellingShingle | Zheng Huang Jiajun Peng Huijuan Lian Jie Guo Weidong Qiu Deep Recurrent Model for Server Load and Performance Prediction in Data Center Complexity |
| title | Deep Recurrent Model for Server Load and Performance Prediction in Data Center |
| title_full | Deep Recurrent Model for Server Load and Performance Prediction in Data Center |
| title_fullStr | Deep Recurrent Model for Server Load and Performance Prediction in Data Center |
| title_full_unstemmed | Deep Recurrent Model for Server Load and Performance Prediction in Data Center |
| title_short | Deep Recurrent Model for Server Load and Performance Prediction in Data Center |
| title_sort | deep recurrent model for server load and performance prediction in data center |
| url | http://dx.doi.org/10.1155/2017/8584252 |
| work_keys_str_mv | AT zhenghuang deeprecurrentmodelforserverloadandperformancepredictionindatacenter AT jiajunpeng deeprecurrentmodelforserverloadandperformancepredictionindatacenter AT huijuanlian deeprecurrentmodelforserverloadandperformancepredictionindatacenter AT jieguo deeprecurrentmodelforserverloadandperformancepredictionindatacenter AT weidongqiu deeprecurrentmodelforserverloadandperformancepredictionindatacenter |