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Online QoS Prediction in the Cloud Environments Using Hybrid Time-Series Data Mining Approach

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Abstract

Considering the diversity of proposed cloud computing services in federated clouds, users should be very well aware of their current required and future expected resources and values of the quality-of-service parameters to compose proper services from a pool of clouds. Various approaches and methods have been proposed to accurately address this issue and predict the quality-of-service parameters. The quality-of-service parameters are stored in the form of time series. Those works mostly discover patterns either between separate time series or inside specific time series and not both aspects together. The main research gap which is covered in this work is to make use of measuring similarities inside the current time series as well as between various time series. This work proposes a novel hybrid approach by means of time-series clustering, minimum description length, and dynamic time warping similarity to analyze user needs and provide the best-fit quality-of-service prediction solution to the users through the multi-cloud. We considered the time as one of our important factors, and the system analyzes the changes over time. Furthermore, our proposed method is a shape-based prediction that uses dynamic time warping for covering geographical time zone differences with the novel preprocessing method using statistically generated semi-real data to fulfill noisy data. The experimental results of the proposed approach show very close predictions to the real values from practices. We achieved about 0.5 mean absolute error rate on average. For this work, we used the WS-DREAM dataset which is widely used in this area.

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Notes

  1. https://github.com/wsdream/wsdream-dataset/tree/master/dataset2.

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Authors and Affiliations

  1. Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

    Amin Keshavarzi & Abolfazl Toroghi Haghighat

  2. Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran

    Mahdi Bohlouli

  3. Research and Innovation Department, Petanux GmbH, Bonn, Germany

    Mahdi Bohlouli

  4. Research Center for Basic Sciences and Modern Technologies (RBST), Institute for Advanced Studies in Basic Sciences, Zanjan, Iran

    Mahdi Bohlouli

Authors
  1. Amin Keshavarzi
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  2. Abolfazl Toroghi Haghighat
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  3. Mahdi Bohlouli
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Correspondence to Abolfazl Toroghi Haghighat.

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Keshavarzi, A., Toroghi Haghighat, A. & Bohlouli, M. Online QoS Prediction in the Cloud Environments Using Hybrid Time-Series Data Mining Approach. Iran J Sci Technol Trans Electr Eng 45, 461–478 (2021). https://doi.org/10.1007/s40998-020-00371-z

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  • DOI: https://doi.org/10.1007/s40998-020-00371-z

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