Deep recurrent model for server load and performance prediction in data center
type: Item
instance of: scholarly article
title: Deep recurrent model for server load and performance prediction in data center
zbMATH Open document ID: 1380.93045
DOI: 10.1155/2017/8584252
author: Zheng Huang
author: Jiajun Peng
author: Huijuan Lian
author: Jie Guo
author: Weidong Qiu
published in: Complexity
publication date: 2018-02-01T00:00:00Z
review text: Summary: Recurrent Neural Network (RNN) has been widely applied to many sequential tagging tasks such as Natural Language Processing (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.
Mathematics Subject Classification ID: 93A30
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93C65
Mathematics Subject Classification ID: 93B40
zbMATH DE Number: 6833400
zbMATH Keywords: recurrent neural network (RNN)
zbMATH Keywords: natural language processing (NLP)
zbMATH Keywords: time series analysis
zbMATH Keywords: server load
zbMATH Keywords: performance prediction
describes a project that uses: MySQL
describes a project that uses: AlexNet
describes a project that uses: ImageNet
describes a project that uses: Nginx
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/8584252
OpenAlex ID: W2768322347
cites work: Hierarchical Forecasting of Web Server Workload Using Sequential Monte Carlo Training
cites work: Multilayer feedforward networks are universal approximators
cites work: Q2934059
description: scientific article; zbMATH DE number 6833400