الفهرس 
Cloud ComputingOnly 14 pages are availabe for public view
 |  | from | 146 |  |  |
| | | from | 146 | | |
Abstract
The next generation of computing infrastructure is moving toward the cloud,
and numerous cloud suppliers offer a wide range of cloud services. It can be very
difficult to determine which cloud services are suitable for a certain application.
In addition to the computing requirements, addressing this difficulty entails
balancing a number of elements, including corporate demands, technology,
policies, and preferences.
Numerous cloud services have been developed as a result of the rapid growth
of cloud computing. Any firm has the choice to adopt cloud services if it wants
to maximize flexibility and respond quickly to market demands. It is a very major
challenge for enterprises to choose the suitable cloud services that may satisfy
their requirements because of the diversity of cloud service providers.
As a result, choosing a cloud service provider can be seen as a form of
decision analysis problem involving several stakeholders. Cloud service
selection helps users choose the most suitable cloud services for their needs and
reduces losses brought on by poor service selection. Therefore, the selection
process of cloud services can be considered as a type of multi-criteria decision
analysis problems.
This thesis introduces a novel framework that can be used for selecting the
most suitable provider in the case of missing values in the evaluation of
alternatives based on a neutrosophic multi-criteria decision analysis (NMCDA)
approach for assessing the quality of cloud services. The framework is composed
of two steps:In first step, The Modified Generative Adversarial Network (M-GAN) is used
in the framework to impute missing data.
GANs are generative models based on the deep learning framework to
generate artificial data. It provides a way to learn deep representations without
extensively annotated training data. GANs comprise a generator and a
discriminator, both trained under the adversarial learning idea. The goal of GANs
is to estimate the potential distribution of real data samples and generate new
samples from that distribution. The modified version of GAN has achieved an
accuracy of nearly 94%.
In second step, the multi-criteria decision-making neutrosophic algorithm that
make evaluation of the alternatives for choosing the best provider in accordance
with various eight criteria (Availability, Throughput, Successibility, Reliability,
Latency, Response time, Response Time of Customer Services, and Cost). Multi
Criteria Decision Making (MCDM) provides strong decision making in domains
where selection of best alternative is highly complex.
According to the experiments done in the thesis, the Novel framework has
achieved success in choosing the most suitable cloud provider.