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research-article

Cloud traffic prediction based on fuzzy ARIMA model with low dependence on historical data

Authors:

Zahra Pooranian,

Paola G. Vinueza NaranjoAuthors Info & Claims

Transactions on Emerging Telecommunications Technologies, Volume 33, Issue 3

https://doi.org/10.1002/ett.3731

Published: 21 March 2022 Publication History

Abstract

Traffic prediction with high accuracy has become a vital and challenging issue for resource management in cloud computing. It should be noted that one of the prominent factors in resource management is accurate traffic prediction based on a few data points and within a short time period. The autoregressive integrated moving average (ARIMA) model is a suitable model to predict traffic in short time periods. However, it requires a massive amount of historical data to achieve accurate results. On the other hand, the fuzzy regression model is adequate for prediction using less historical data. Aforementioned by these considerations, in this paper, a combination of ARIMA and fuzzy regression called fuzzy autoregressive integrated moving average (FARIMA) is used to forecast traffic in cloud computing. Besides, we adopt the FARIMA model by using the sliding window, called SOFA, concept to determine models with higher prediction accuracy. Accuracy comparison of these models based on the root means square error and coefficient of determination demonstrates that SOFA is about 5.4 and 0.009, respectively, which is the superior model for traffic prediction.

Graphical Abstract

1. We design FARIMA model and SOFA algorithm as input data for the sliding window phenomenon (see FIGURE 2).

2. We present the architecture of SOFA algorithm and explain its components.

3. We validate our proposed SOFA algorithm through various input data to visualize the multiple forecasts against the predicted valued in the ARIMA.

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Cited By

Feng RCui HFeng QChen SGu XYao B(2023)Urban Traffic Congestion Level Prediction Using a Fusion-Based Graph Convolutional NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330408924:12(14695-14705)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3304089
Hu ZRuan KYu WChen S(2023)Enhanced edge convolution-based spatial-temporal network for network traffic predictionApplied Intelligence10.1007/s10489-023-04626-053:19(22031-22043)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04626-0
Lohrasbinasab IShahraki ATaherkordi ADelia Jurcut A(2022)From statistical‐ to machine learning‐based network traffic predictionTransactions on Emerging Telecommunications Technologies10.1002/ett.439433:4Online publication date: 17-Apr-2022
https://dl.acm.org/doi/10.1002/ett.4394

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Cloud traffic prediction based on fuzzy ARIMA model with low dependence on historical data

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Published In

cover image Transactions on Emerging Telecommunications Technologies

Transactions on Emerging Telecommunications Technologies Volume 33, Issue 3

March 2022

640 pages

EISSN:2161-3915

DOI:10.1002/ett.v33.3

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© 2019 John Wiley & Sons, Ltd.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 21 March 2022

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Cited By

Feng RCui HFeng QChen SGu XYao B(2023)Urban Traffic Congestion Level Prediction Using a Fusion-Based Graph Convolutional NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330408924:12(14695-14705)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3304089
Hu ZRuan KYu WChen S(2023)Enhanced edge convolution-based spatial-temporal network for network traffic predictionApplied Intelligence10.1007/s10489-023-04626-053:19(22031-22043)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04626-0
Lohrasbinasab IShahraki ATaherkordi ADelia Jurcut A(2022)From statistical‐ to machine learning‐based network traffic predictionTransactions on Emerging Telecommunications Technologies10.1002/ett.439433:4Online publication date: 17-Apr-2022
https://dl.acm.org/doi/10.1002/ett.4394

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