A trust framework utilization in cloud computing environment based on multi-criteria decision-making methods

Faculty Computer Science Year: 2021
Type of Publication: ZU Hosted Pages: Pages 997-1005
Authors:
Journal: The Computer Journal Oxford University Press Volume: Volume 65
Keywords : , trust framework utilization , cloud computing environment    
Abstract:
Cloud computing (CC) is a complete online system of specialized technical resources for data computing and storage management. Many organizations, including small and start-ups, have recommended using CC technology rather than building their own high-costed data centers. Due to the diversity of cloud service providers, identifying appropriate cloud services that can meet users’ and organizations’ requirements, including trust, is a major challenge. In this paper, we propose a trust framework utilization to evaluate cloud services trust using multi-criteria decision-making and fuzzy logic techniques from different perspectives based on performance, agility, finance, security and usability criteria. The comparison of obtained results with related ones shows that ours are efficient and promising decisions for cloud users and organizations as well.
   
     
 
       

Author Related Publications

  • Ibrahiem Mahmoud Mohamed Elhenawy, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "Determining Extractive Summary for a Single Document Based on Collaborative Filtering Frequency Prediction and Mean Shift Clustering", International Association of Engineers, 2019 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "A Review on the Applications of Neutrosophic Sets", Source: Journal of Computational and Theoretical Nanoscience, Volume 13, Number 1, January 2016, pp. 936-944(9), 2016 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization", Springer, 2017 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "Solving 0–1 knapsack problem by binary flower pollination algorithm", Springer, 2018 More

Department Related Publications

  • Ibrahiem Mahmoud Mohamed Elhenawy, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ahmed Raafat Abass Mohamed Saliem, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010 More
  • Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013 More
  • Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012 More
Tweet