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Cloud service selection using multicriteria decision analysis.

Md Whaiduzzaman1, Abdullah Gani1, Nor Badrul Anuar1

  • 1Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.

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Summary
This summary is machine-generated.

This study explores multicriteria decision analysis (MCDA) for cloud computing (CC) service selection. It provides a comprehensive overview and taxonomy of MCDA techniques for choosing optimal cloud services.

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Area of Science:

  • Computer Science
  • Information Technology
  • Operations Research

Background:

  • Cloud computing (CC) offers scalable, on-demand services, making service selection critical for users.
  • Effective service provisioning in CC requires methods to match user needs with available services.
  • Multicriteria decision analysis (MCDA) is a key approach for addressing complex selection problems.

Purpose of the Study:

  • To analyze the application of multicriteria decision analysis (MCDA) techniques for cloud computing (CC) service selection.
  • To synthesize and present a comprehensive overview of MCDA methods relevant to CC.
  • To provide a taxonomy of MCDA techniques based on a thorough literature review.

Main Methods:

  • Literature review and synthesis of existing research on MCDA in cloud computing.
  • Identification and categorization of various MCDA techniques applicable to service selection.
  • Comparative analysis of the suitability and practical implementation of different MCDA methods.

Main Results:

  • A taxonomy of MCDA techniques for cloud computing service selection is presented.
  • Several state-of-the-art MCDA methods are analyzed for their effectiveness in CC.
  • The study highlights practical aspects and suitability of MCDA for diverse cloud scenarios.

Conclusions:

  • MCDA is a valuable framework for optimizing cloud computing service selection.
  • The presented taxonomy and analysis aid researchers and practitioners in choosing appropriate MCDA techniques.
  • Further research can leverage this work to enhance automated and intelligent service selection in cloud environments.