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QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach.

L Jayakumar1, R Jothi Chitra2, J Sivasankari3

  • 1Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India.

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Summary
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This study introduces a multicriteria optimization approach for cloud virtual machine placement, utilizing genetic algorithms to tackle complex problems. It compares these algorithms against greedy methods for efficient cloud resource management.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Operations Research

Background:

  • Cloud computing environments face challenges in optimal virtual machine placement.
  • Placement problems are often computationally intractable in polynomial time.
  • Quality of Service (QoS) modeling is crucial for efficient resource allocation.

Purpose of the Study:

  • To present a multicriteria optimization approach for cloud virtual machine placement.
  • To define parameters and metrics for a precise analysis of placement issues.
  • To evaluate the effectiveness of genetic algorithms compared to traditional methods.

Main Methods:

  • Developed a multicriteria optimization model using genetic algorithms.
  • Implemented two versions of the genetic algorithm: one for elementary services and one for compound services.
  • Compared genetic algorithms against two greedy algorithms: round-robin and best-fit sorted.

Main Results:

  • The proposed genetic algorithm models provide a satisfactory solution for the impractical optimal placement problem.
  • The study details the implementation and characteristics of both genetic and greedy algorithms.
  • QoS modeling parameters and metrics enhance the analysis of cloud placement algorithms.

Conclusions:

  • Genetic algorithms offer a viable meta-heuristic approach for solving complex virtual machine placement problems in the cloud.
  • The defined QoS modeling framework supports a more precise analysis of cloud resource allocation strategies.
  • Comparative analysis highlights the performance trade-offs between genetic and greedy placement algorithms.