Evaluation of Parameters Importance in Cloud Service Selection Using Rough Sets

With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emerging. It is a key issue to assess the cloud services in order to help the cloud users select the most suitable cloud service and the cloud providers offer this service with the highest quality. The criteria parameters defining the cloud services are complex which lead to cloud service deviation. In this paper, we propose an assessment method of parameters importance in cloud services using rough set theory. The method can effectively compute the importance of cloud services parameters and sort them. On the one hand, the calculation can be used as the credible reference when users choose their appropriate cloud services. On the other hand, it can help cloud service providers to meet user requirements and enhance the user experience. The simulation results show the effectiveness of the method and its relevance in the cloud context.

[1]  Eui-nam Huh,et al.  Efficient service recommendation system for cloud computing market , 2009, ICIS.

[2]  Andrew Lipsman,et al.  The Power of “Like” , 2012, Journal of Advertising Research.

[3]  Youcef Aklouf,et al.  An Approach based on user preferences for selecting SaaS product , 2014, 2014 International Conference on Multimedia Computing and Systems (ICMCS).

[4]  Prasad Saripalli,et al.  MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[5]  Rajkumar Buyya,et al.  2011 Fourth IEEE International Conference on Utility and Cloud Computing SMICloud: A Framework for Comparing and Ranking Cloud Services , 2022 .

[6]  Leïla Merghem,et al.  Cloud service selection based on rough set theory , 2014, 2014 International Conference and Workshop on the Network of the Future (NOF).

[7]  Raouf Boutaba,et al.  Assessing Software Service Quality and Trustworthiness at Selection Time , 2010, IEEE Transactions on Software Engineering.

[8]  G. Nie,et al.  Evaluation Index System of Cloud Service and the Purchase Decision- Making Process Based on AHP , 2011 .

[9]  Jonas Repschläger,et al.  Developing a Cloud Provider Selection Model , 2011, EMISA.

[10]  Shrikant Mulik,et al.  An Approach for Selecting Software-as-a-Service (SaaS) Product , 2009, 2009 IEEE International Conference on Cloud Computing.

[11]  Wei-Wen Wu,et al.  Mining significant factors affecting the adoption of SaaS using the rough set approach , 2011, J. Syst. Softw..

[12]  Michael E. Raynor,et al.  The Innovator's Solution: Creating and Sustaining Successful Growth , 2003 .

[13]  Yun Peng,et al.  A framework to canonicalize manufacturing service capability models , 2015, Comput. Ind. Eng..

[14]  Ali Miri,et al.  An End-to-End QoS Mapping Approach for Cloud Service Selection , 2013, 2013 IEEE Ninth World Congress on Services.

[15]  Frank Teuteberg,et al.  Costing of Cloud Computing Services: A Total Cost of Ownership Approach , 2012, 2012 45th Hawaii International Conference on System Sciences.

[16]  Z. Pawlak,et al.  Rough sets perspective on data and knowledge , 2002 .

[17]  Markus Helfert,et al.  Towards the Development of a Cloud Service Capability Assessment Framework , 2014 .

[18]  Engin Kirda,et al.  A security analysis of Amazon's Elastic Compute Cloud service , 2012, IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN 2012).

[19]  Daniel Moldovan,et al.  ADVISE - A Framework for Evaluating Cloud Service Elasticity Behavior , 2014, ICSOC.

[20]  T. Saaty How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[21]  David H. Cropley,et al.  Diagnosing Organizational Innovation: Measuring the Capacity for Innovation , 2013 .

[22]  Thomas L. Saaty,et al.  The Analytic Hierarchy and Analytic Network Processes for the Measurement of Intangible Criteria and for Decision-Making , 2016 .

[23]  Germano Lambert-Torres,et al.  Rough Set Theory - Fundamental Concepts, Principals, Data Extraction, and Applications , 2009 .

[24]  Patrick De Pelsmacker,et al.  The influence of ad-evoked feelings on brand evaluations: : Empirical generalizations from consumer responses to more than 1000 TV commercials , 2013 .