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Categorization of Cloud Workload Types with Clustering

  • Piotr Orzechowski
  • Jerzy Proficz
  • Henryk Krawczyk
  • Julian Szymański
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 395)

Abstract

The paper presents a new classification schema of IaaS cloud workloads types, based on the functional characteristics. We show the results of an experiment of automatic categorization performed with different benchmarks that represent particular workload types. Monitoring of resource utilization allowed us to construct workload models that can be processed with machine learning algorithms. The direct connection between the functional classes and the resource utilization was shown, using unsupervised categorization approach based on moving average for finding a class number, and k-means algorithm for clustering.

Keywords

Workload categorization IaaS Cloud computing Clustering Cloud load prediction 

Notes

Acknowledgments

This work was carried out as a part of the research project “Recommendation Component for Intelligent Computing Clouds”, co-financed by the European Regional Development Fund, Innovative Economy Operational Programme 2007–2013, Priority Axis 1: “Research and development of state-of-the-art technologies”. The experiments were performed using high-performance computing infrastructure provided by the Academic Computer Centre in Gdansk (CI TASK).

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Copyright information

© Springer India 2017

Authors and Affiliations

  • Piotr Orzechowski
    • 1
  • Jerzy Proficz
    • 1
  • Henryk Krawczyk
    • 2
  • Julian Szymański
    • 2
  1. 1.Academic Computer Centre in Gdansk (CI TASK)GdańskPoland
  2. 2.Department of Computer Systems ArchitectureGdańsk University of TechnologyGdańskPoland

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