G-TOPSIS: a cloud service selection framework using Gaussian TOPSIS for rank reversal problem

2020 ◽  
Vol 77 (1) ◽  
pp. 523-562
Author(s):  
Rohit Kumar Tiwari ◽  
Rakesh Kumar
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Selection Framework

A Robust and Efficient MCDM-Based Framework for Cloud Service Selection Using Modified TOPSIS

2021 ◽  
Vol 11 (1) ◽  
pp. 21-51
Author(s):  
Rohit Kumar Tiwari ◽  
Rakesh Kumar
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Reversal Phenomenon ◽  
Optimal Service ◽  
Selection Framework ◽  
Cloud Users

Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.

Fuzzy cloud service selection framework

2014 ◽  
Author(s):  
Masoumeh Tajvidi ◽  
Rajiv Ranjan ◽  
Joanna Kolodziej ◽  
Lizhe Wang
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Selection Framework

scholarly journals BSS: A Brokering Model for Service Selection using Integrated Weighting approach in Federated Cloud

2021 ◽  
Author(s):  
Sameer Singh Chauhan ◽  
Emmanuel S Pilli ◽  
R C Joshi
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Entropy Method ◽  
Cloud Services ◽  
Total Weight ◽  
Reversal Problem ◽  
Cloud Broker ◽  
Subjective Weight ◽  
Access Patterns ◽  
Application Requirements

Abstract Federated Cloud is a multi-cloud platform that integrates various cloud providers either through standardization or an agreement. The services offered by different federation members possess different access patterns, similar characteristics, varied performance levels, different costs, etc. This heterogeneity creates a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service and federation management to help both service provider and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users' feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon's Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.

scholarly journals BSS: a brokering model for service selection using integrated weighting approach in cloud environment

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sameer Singh Chauhan ◽  
Emmanuel S. Pilli ◽  
R. C. Joshi
Keyword(s):  
Service Management ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Total Weight ◽  
Cost Models ◽  
Reversal Problem ◽  
Cloud Broker ◽  
Subjective Weight ◽  
Cloud Providers

AbstractCloud providers shares their resources and services through collaboration in order to increase resource utilization, profit and quality of services. The offered services with different access patterns, similar characteristics, varied performance levels and cost models create a heterogeneous service environment. It becomes a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service management to help both cloud providers and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users’ feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon’s Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.

QoCS and Cost Based Cloud Service Selection Framework

2017 ◽  
Vol 48 (3) ◽  
pp. 167-172 ◽  
Author(s):  
Sarvesh Pandey ◽  
◽  
A K Daniel
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Selection Framework

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection

2018 ◽  
Vol 108 ◽  
pp. 339-354 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Kalpana V. ◽  
Kannan Kirthivasan ◽  
Shankar Sriram V.S.
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Service Selection ◽  
Cloud Service ◽  
Trust Prediction ◽  
Prediction Approach

A rough set-based hypergraph trust measure parameter selection technique for cloud service selection

2017 ◽  
Vol 73 (10) ◽  
pp. 4535-4559 ◽  
Author(s):  
Nivethitha Somu ◽  
Kannan Kirthivasan ◽  
V. S. Shankar Sriram
Keyword(s):  
Rough Set ◽  
Service Selection ◽  
Cloud Service ◽  
Parameter Selection ◽  
Selection Technique ◽  
Trust Measure
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