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

Appeared in IEEE transactions on cloud computing .

Abstract

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 paper 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% on average compared to the ARIMA, the triple exponential
smoothing model and ANN+SaDE model respectively with a small time overhead.

Publication date:
April 2020

Authors:
Yulai Xie
Minpeng Jin
Zhuping Zou
Gongming Xu
Dan Feng
Wenmao Liu
Darrell D. E. Long

Projects:

Full paper text: Not currently available for download

Bibtex entry

@article{xie-tcc2020,
  author       = {Yulai Xie and Minpeng Jin and Zhuping Zou and Gongming Xu and Dan Feng and Wenmao Liu and Darrell D. E. Long},
  title        = {Real-time Prediction of Docker Container Resource Load Based on A Hybrid Model of {ARIMA} and Triple Exponential Smoothing},
  journal      = {IEEE transactions on cloud computing},
  volume       = {},
  month        = apr,
  year         = {2020},
}
Last modified 15 Jul 2020