Checkpoints and Requirements Based Cloud Service Ranking

2018 ◽  
pp. 3-15
Author(s):  
Mohammad Riyaz Belgaum ◽  
Shahrulniza Musa ◽  
Muhammad Alam ◽  
Mazliham Mohd Su’ud ◽  
Safeeullah Soomro ◽  
...  
Keyword(s):  
Cloud Service ◽  
Service Ranking

scholarly journals Cloud Service ranking and Selection using Linear Programming

2015 ◽  
Vol 124 (3) ◽  
pp. 39-43 ◽  
Author(s):  
J. Preethi ◽  
N. Sujaudeen ◽  
P. Venugopal ◽  
T.T. Mirnalinee
Keyword(s):  
Linear Programming ◽  
Cloud Service ◽  
Ranking And Selection ◽  
Service Ranking

GCRITICPA: A CRITIC and Grey Relational Analysis Based Service Ranking Approach for Cloud Service Selection

2018 ◽  
pp. 3-16
Author(s):  
Gireesha Obulaporam ◽  
Nivethitha Somu ◽  
Gauthama Raman ManiIyer Ramani ◽  
Akshya Kaveri Boopathy ◽  
Shankar Sriram Vathula Sankaran
Keyword(s):  
Grey Relational Analysis ◽  
Service Selection ◽  
Cloud Service ◽  
Relational Analysis ◽  
Service Ranking ◽  
Grey Relational

scholarly journals W_SR: A QoS Based Ranking Approach for Cloud Computing Service

2014 ◽  
Vol 3 (2) ◽  
pp. 55-62 ◽  
Author(s):  
Arezoo Jahani ◽  
Leyli Mohammad Khanli ◽  
Seyed Naser Razavi
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Quality Of Services ◽  
Computing Model ◽  
Cloud Computing Service ◽  
The World ◽  
Service Ranking ◽  
S Application

Cloud computing is a kind of computing model that promise accessing to information resources in request time and subscription basis. In this environment, there are different type of user’s application with different requirements. In addition, there are different cloud Service providers which present spate services with various qualitative traits. Therefore determining the best cloud computing service for users with specific applications is a serious problem. Service ranking system compares the different services based on quality of services (QoS), in order to select the most appropriate service. In this paper, we propose a W_SR (Weight Service Rank) approach for cloud service ranking that uses from QoS features. Comprehensive experiments are conducted employing real-world QoS dataset, including more than 2500 web services over the world. The experimental results show that execution time of our approach is less than other approaches and it is more flexible and scalable than the others with increase in services or users.

A trust centric optimal service ranking approach for cloud service selection

2018 ◽  
Vol 86 ◽  
pp. 234-252 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Kannan Kirthivasan ◽  
Shankar Sriram V.S.
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Optimal Service ◽  
Service Ranking

CloudRank

2013 ◽  
pp. 230-258 ◽  
Author(s):  
Jianxin Li ◽  
Linlin Meng ◽  
Zekun Zhu ◽  
Xudong Li ◽  
Jinpeng Huai ◽  
...  
Keyword(s):  
Fault Location ◽  
Cloud Service ◽  
User Feedback ◽  
User Preferences ◽  
Cloud Services ◽  
Testing Environment ◽  
Service Testing ◽  
Active Service ◽  
Service Ranking ◽  
Web Service Testing

In this chapter, the authors propose a Cloud service ranking system, named CloudRank, based on both the user feedback and service testing. In CloudRank, we design a new ranking-oriented collaborative filtering (CF) approach named WSRank, in which user preferences are modeled as personal rankings derived from user QoS ratings on services to address service quality predication problem. Different from the existing similar approaches, WSRank firstly presents a QoS model which allows users to express their preferences flexibly while providing combination of multiple QoS properties to give an overall rating to a service. Secondly, it measures the similarity among users based on the correlation of their rankings of services rather than the rating values. Nevertheless, it is neither accurate nor sufficient to rank Cloud services merely based on users’ feedbacks, as there are many problems such as cold-start problem, absence of user feedback, even some service faults occurred in a service workflow, so to get an accurate ranking, an active service QoS testing and fault location approach is required together with WSRank. Therefore, in CloudRank, the authors also designed an automated testing prototype named WSTester to collect real QoS information of services. WSTester integrates distributed computers to construct a virtual testing environment for Web service testing and deploys test tasks onto distributed computers efficiently.

CROSA: Context‐aware cloud service ranking approach using online reviews based on sentiment analysis

2019 ◽  
pp. e5358 ◽  
Author(s):  
Emna Ben‐Abdallah ◽  
Khouloud Boukadi ◽  
Jaime Lloret ◽  
Mohamed Hammami
Keyword(s):  
Sentiment Analysis ◽  
Cloud Service ◽  
Online Reviews ◽  
Context Aware ◽  
Service Ranking
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