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Temporal Pattern Based QoS Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10042)

Abstract

Quality-of-Service (QoS) is critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services are unfixed and highly related to the service status and network environments which are variable against time, it is critical to obtain the missing QoS values of candidate services at given time intervals. In this paper, we propose a temporal pattern based QoS prediction approach to address this challenge. Clustering approach is utilized to find the temporal patterns based on services QoS curves over time series, and polynomial fitting function is employed to predict the missing QoS values at given time intervals. Furthermore, a data smoothing process is employed to improve prediction accuracy. Comprehensive experiments based on a real world QoS dataset demonstrate the effectiveness of the proposed prediction approach.

Keywords

Service Computing QoS prediction Temporal pattern 

Notes

Acknowledgement

This research was made possible by NPRP 9-224-1-049 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. This research was partially supported by the Natural Science Foundation of China under grant of No. 61379119, Science and Technology Program of Zhejiang Province under grant of No. 2013C01073, the Open Project of Qihoo360 under grant of No. 15-124002-002.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Computer Science and Information TechnologyRMITMelbourneAustralia
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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