Abstract

Cloud computing performance optimization is the process of increasing the performance of cloud services at minimum cost, based on various features. In this paper, we present a new approach called MOOA-CSF (Multi-Objective Optimization Approach for Cloud Services Finding), which uses supervised learning and multi-criteria decision techniques to optimize price and performance in cloud computing. Our system uses an artificial neural network (ANN) to classify a set of cloud services. The inputs of the ANN are service features, and the classification results are three classes of cloud services: one that is favorable to the client, one that is favorable to the system, and one that is common between the client and system classes. The ELECTRE (ÉLimination Et Choix Traduisant la REalité) method is used to order the services of the three classes. We modified the genetic algorithm (GA) to make it adaptive to our system. Thus, the result of the GA is a hybrid cloud service that theoretically exists, but practically does not. To this end, we use similarity tests to calculate the level of similarity between the hybrid service and the other benefits in both classes. MOOA-CSF performance is evaluated using different scenarios. Simulation results prove the efficiency of our approach.

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Cite this article

@article{cai-6395,
  author  = {Youcef Bezza and Ouassila Hioual and Ouided Hioual and Derya Yiltas-Kaplan and Zeynep Gürkaş-Aydin},
  title   = {MOOA-CSF: A Multi-Objective Optimization Approach for Cloud Services Finding},
  journal = {Computing and Informatics},
  volume  = {42},
  number  = {4},
  pages   = {896--922},
  year    = {2023},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2023_4_896}
}
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