Temporal-Perturbation aware Reliability Sensitivity Measurement for Adaptive Cloud Service Selection

2021 ◽  
Author(s):  
Lei Wang ◽  
Qiang He ◽  
Demin Gao ◽  
Jing Wan ◽  
Yunqiu Zhang
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Sensitivity Measurement ◽  
Reliability Sensitivity ◽  
Temporal Perturbation

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

scholarly journals An MCDM method for cloud service selection using a Markov chain and the best-worst method

2018 ◽  
Vol 159 ◽  
pp. 120-131 ◽  
Author(s):  
Falak Nawaz ◽  
Mehdi Rajabi Asadabadi ◽  
Naeem Khalid Janjua ◽  
Omar Khadeer Hussain ◽  
Elizabeth Chang ◽  
...  
Keyword(s):  
Markov Chain ◽  
Service Selection ◽  
Cloud Service ◽  
Mcdm Method ◽  
Best Worst Method

Client-Centric Cloud Service Composition

2017 ◽  
pp. 144-166
Author(s):  
Vivek Gaur ◽  
Praveen Dhyani ◽  
Om Prakash Rishi
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Processes ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Quality Parameters ◽  
End User ◽  
Cloud Service Composition ◽  
Tabu Search Methods

Recent computing world has seen rapid growth of the number of middle and large scale enterprises that deploy business processes sharing variety of services available over cloud environment. Due to the advantage of reduced cost and increased availability, the cloud technology has been gaining unbound popularity. However, because of existence of multiple cloud service providers on one hand and varying user requirements on the other hand, the task of appropriate service composition becomes challenging. The conception of this chapter is to consider the fact that different quality parameters related to various services might bear varied importance for different user. This chapter introduces a framework for QoS-based Cloud service selection to satisfy the end user needs. A hybrid algorithm based on genetic algorithm (GA) and Tabu Search methods has been developed, and its efficacy is analysed. Finally, this chapter includes the experimental analysis to present the performance of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document