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A deep learning approach for collaborative prediction of Web service QoS

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Abstract

Web services are a cornerstone of many crucial domains, including cloud computing and the Internet of Things. In this context, QoS prediction for Web services is a highly important and challenging issue that facilitates the building of value-added processes such as compositions and workflows of services. Current QoS prediction approaches, like collaborative filtering methods, mainly suffer from obstacles related to data sparsity and the cold-start problem. Moreover, previous studies have not conducted in-depth explorations of the impact of the geographical characteristics of services/users and QoS ratings on the prediction problem. To address these difficulties, we propose a deep-learning-based approach for QoS prediction. The main idea consists of combining a matrix factorization model based on a deep autoencoder (DAE) with a clustering technique based on geographical characteristics to improve the effectiveness of prediction. The overall method proceeds as follows: first, we cluster the input QoS data using a self-organizing map that incorporates knowledge of geographical neighborhoods; by doing so, we can reduce the data sparsity while preserving the topology of the input data. In addition, the clustering step effectively overcomes the cold-start problem. Next, we train a DAE that minimizes the squared loss between the ground-truth QoS and the predicted one, for each cluster. Finally, the unknown QoS of a new service is predicted using the trained DAE related to the closest cluster. To evaluate the effectiveness and robustness of our approach, we conducted a comprehensive set of experiments based on a real-world Web service QoS dataset. The experimental results show that our method achieves better prediction performance than several state-of-the-art methods.

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Notes

  1. The autoencoder is called linear if the transfer functions are linear, otherwise it is nonlinear.

  2. SOM training stops when the weight vectors are stabilized or the maximum number of iterations is reached.

  3. An AS is either a single network or a group of networks that is controlled by a common network administrator (or group of administrators) on behalf of a single administrative entity (university, a business enterprise, etc.)

  4. https://github.com/peterwittek/somoclu.

  5. Maxmind GeoIP2 Geolocational Databases. Retrieved on May 2019 from http://dev.maxmind.com/geoip/geoip2/geolite2/.

  6. IP2Location LITE Databases. Retrieved on May 2019 from http://lite.ip2location.com.

  7. For this work, the clustering is performed on country ID. In order to have only eight clusters, we grouped some of countries in the same cluster

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Acknowledgements

This work has been performed with the support of the High Performance Computing Platform MESO@LR, financed by the Occitanie / Pyrénées-Méditerranée Region, Montpellier Mediterranean Metropole and the University of Montpellier, France.

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Authors and Affiliations

  1. LRIT, Computer Science Department, University of Tlemcen, Tlemcen, Algeria

    Mohammed Ismail Smahi, Fethallah Hadjila & Abdelkrim Benamar

  2. LIRMM, University of Montpellier and CNRS, Montpellier, France

    Mohammed Ismail Smahi & Chouki Tibermacine

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  1. Mohammed Ismail Smahi
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  2. Fethallah Hadjila
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  4. Abdelkrim Benamar
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Correspondence to Mohammed Ismail Smahi.

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Smahi, M.I., Hadjila, F., Tibermacine, C. et al. A deep learning approach for collaborative prediction of Web service QoS. SOCA 15, 5–20 (2021). https://doi.org/10.1007/s11761-020-00304-y

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