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Complexity
Volume 2017 (2017), Article ID 8584252, 10 pages
https://doi.org/10.1155/2017/8584252
Research Article

Deep Recurrent Model for Server Load and Performance Prediction in Data Center

1School of Cyber Security, Shanghai Jiao Tong University, Shanghai, China
2Westone Cryptologic Research Center, Beijing 100070, China

Correspondence should be addressed to Jiajun Peng; nc.ude.utjs@jjp

Received 31 August 2017; Accepted 2 November 2017; Published 26 November 2017

Academic Editor: Jia Wu

Copyright © 2017 Zheng Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.