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A new efficient approach for extracting the closed episodes for workload prediction in cloud

  • Maryam Amiri
  • Leyli Mohammad-KhanliEmail author
  • Raffaela Mirandola
Article
  • 3 Downloads

Abstract

The prediction of the future workload of applications is an essential step guiding resource provisioning in cloud environments. In our previous works, we proposed two prediction models based on pattern mining. This paper builds on our previous experience and focuses on the issue of time and space complexities of the prediction model. Specifically, it presents a general approach to improve the efficiency of the pattern mining engine, which leads to improving the efficiency of the predictors. The approach is composed of two steps: (1) Firstly, to improve space complexity, redundant occurrences of patterns are defined and algorithms are suggested to identify and omit them. (2) To improve time complexity, a new data structure, called closed pattern backward tree, is presented for mining closed patterns directly. The approach not only improves the efficiency of our predictors, but also can be employed in different fields of pattern mining. The performance of the proposed approach is investigated based on real and synthetic workloads of cloud. The experimental results show that the proposed approach could improve the efficiency of the pattern mining engine significantly in comparison to common methods to extract closed patterns.

Keywords

Closed episode Cloud computing Prediction Pattern mining engine Workload 

Mathematics Subject Classification

68T10 62-07 

Notes

Acknowledgements

The GWA-T-12 Bitbrains traces are provided by Bitbrains IT Services Inc., which is a service provider that specializes in managed hosting and business computation for enterprises. We thank the GWA team and all those who have graciously provided the data for us.

Supplementary material

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Maryam Amiri
    • 1
  • Leyli Mohammad-Khanli
    • 2
    Email author
  • Raffaela Mirandola
    • 3
  1. 1.Department of Computer Engineering, Faculty of EngineeringArak UniversityArakIran
  2. 2.Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
  3. 3.Dipartimento di ElettronicaInformazione e Bioingegneria Politecnico di MilanoMilanItaly

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