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29 September 2021
A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing
Baogui Xin(
)
)
Keywords:
edge computing, QoS prediction, AutoRegressive Integrated Moving Average (ARIMA), truncated Singular Value Decomposition (SVD)
Cite this article:
Yan C, Zhang Y, Zhong W, et al.
A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing.
Tsinghua Science and Technology,
2022, 27(2): 315-324.
https://doi.org/10.26599/TST.2021.9010040
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In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.
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A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing
Baogui Xin(
)
)
Abstract
In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.
Keywords:
edge computing, QoS prediction, AutoRegressive Integrated Moving Average (ARIMA), truncated Singular Value Decomposition (SVD)
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Received: 29 January 2021
Revised: 17 May 2021
Accepted: 30 May 2021
Published:
29 September 2021
Issue date: April 2022
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© The author(s) 2022
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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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