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QoS Prediction via Multi-scale Feature Fusion Based on Convolutional Neural Network

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Service-Oriented Computing (ICSOC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14419))

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Abstract

Quality of Service (QoS) prediction is a crucial aspect in service management. However, the existing QoS prediction methods face several limitations, such as loss of information during encoding, incomplete feature extraction and neglect of the interaction between features. To this end, this paper proposes a new QoS PRediction method based on a Multi-Scale convolutional neural Network, i.e., QPRMSN. For each service invocation, we build a feature matrix that encodes invocation context and QoS characteristics by using status codes with degrees of membership. Then, a multi-scale convolutional neural network is employed to extract features that keep detailed information during deep global features mining. Moreover, we introduce attention mechanism to learn the intrinsic relationships between features to strengthen key features. Finally, QPRMSN completes the QoS prediction based on a multi-level feature matrix. Extensive experiments are conducted on a real-world dataset to evaluate the performance of QPRMSN. The experimental results demonstrate that QPRMSN outperforms the state-of-the-art QoS prediction models and is better at QoS context encoding.

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Notes

  1. 1.

    https://github.com/bearflying/QPRMSN.

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Acknowledgements

This work is partially supported by China NSF (No. 61202091) and China NSF (No. 62171155).

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Hanzhi Xu, Yanjun Shu, Zhan Zhang & Decheng Zuo

Authors
  1. Hanzhi Xu
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  2. Yanjun Shu
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  3. Zhan Zhang
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  4. Decheng Zuo
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Corresponding author

Correspondence to Decheng Zuo .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Flavia Monti

  2. Technical University of Munich, Garching, Germany

    Stefanie Rinderle-Ma

  3. University of Seville, Seville, Spain

    Antonio Ruiz Cortés

  4. Sun Yat-sen University, Guangzhou, China

    Zibin Zheng

  5. Sapienza University of Rome, Rome, Italy

    Massimo Mecella

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Xu, H., Shu, Y., Zhang, Z., Zuo, D. (2023). QoS Prediction via Multi-scale Feature Fusion Based on Convolutional Neural Network. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14419. Springer, Cham. https://doi.org/10.1007/978-3-031-48421-6_9

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  • DOI: https://doi.org/10.1007/978-3-031-48421-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48420-9

  • Online ISBN: 978-3-031-48421-6

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