Real-Time Prediction of Docker Container Resource Load Based on a Hybrid Model of ARIMA and Triple Exponential Smoothing

More and more enterprises are beginning to use Docker containers to build cloud platforms. Predicting the resource usage of container workload has been an important and challenging problem to improve the performance of cloud computing platform. The existing prediction models either incur large time...

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Bibliographic Details
Published inIEEE transactions on cloud computing Vol. 10; no. 2; pp. 1386 - 1401
Main Authors Xie, Yulai, Jin, Minpeng, Zou, Zhuping, Xu, Gongming, Feng, Dan, Liu, Wenmao, Long, Darrell
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Autoregressive models
Cloud computing
Computational modeling
Containers
Docker container
hybrid model
Load modeling
Optimization
prediction
Prediction models
Predictive models
Real time
Resource scheduling
Smoothing
Smoothing methods
Time series analysis
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Summary:More and more enterprises are beginning to use Docker containers to build cloud platforms. Predicting the resource usage of container workload has been an important and challenging problem to improve the performance of cloud computing platform. The existing prediction models either incur large time overhead or have insufficient accuracy. This article proposes a hybrid model of the ARIMA and triple exponential smoothing. It can accurately predict both linear and nonlinear relationships in the container resource load sequence. To deal with the dynamic Docker container resource load, the weighting values of the two single models in the hybrid model are chosen according to the sum of squares of their predicted errors for a period of time. We also design and implement a real-time prediction system that consists of the collection, storage, prediction of Docker container resource load data and scheduling optimization of CPU and memory resource usage based on predicted values. The experimental results show that the predicting accuracy of the hybrid model improves by 52.64, 20.15, and 203.72 percent on average compared to the ARIMA, the triple exponential smoothing model and ANN+SaDE model respectively with a small time overhead.
ISSN:2168-7161
2168-7161
2372-0018
DOI:10.1109/TCC.2020.2989631