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Performance based Ranking Model for Cloud SaaS Services
Author(s) -
Sahar Abdalla Elmubarak,
Adil Yousif,
Mohammed Bakri Bashir
Publication year - 2017
Publication title -
international journal of information technology and computer science
Language(s) - English
Resource type - Journals
eISSN - 2074-9015
pISSN - 2074-9007
DOI - 10.5815/ijitcs.2017.01.08
Subject(s) - cloud computing , computer science , ranking (information retrieval) , provisioning , software as a service , task (project management) , field (mathematics) , cloud testing , service provider , service (business) , software , database , data mining , machine learning , cloud computing security , software development , computer network , operating system , systems engineering , mathematics , economy , pure mathematics , engineering , economics
Cloud computing systems provide virtualized resources that can be provisioned on demand basis. Enormous number of cloud providers are offering diverse number of services. The performance of these services is a critical factor for clients to determine the cloud provider that they will choose. However, determining a provider with efficient and effective services is a challenging task. There is a need for an efficient model that help clients to select the best provider based on the performance attributes and measurements. Cloud service ranking is a standard method used to perform this task. It is the process of arranging and classifying several cloud services within the cloud, then compute the relative ranking values of them based on the quality of service required by clients and the features of the cloud services. The objective of th is study is to propose an enhanced performance based ranking model to help users choose the best service they need. The proposed model combines the attributes and measurements from cloud computing field and the welldefined and established software engineering field. SMICloud Toolkit has been used to test the applicability of the proposed model. The experimentation results of the proposed model were promising.

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