Cloud Services Composition Support by Using Semantic Annotation and Linked Data

Author(s):  
Martín Serrano ◽  
Lei Shi ◽  
Mícheál Ó Foghlú ◽  
William Donnelly
2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wu ◽  
Naixue Xiong ◽  
Yannong Huang ◽  
Qiong Gu ◽  
Chunyang Hu ◽  
...  

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.


2014 ◽  
Vol 55 ◽  
pp. 29-42 ◽  
Author(s):  
Juan C. Vidal ◽  
Manuel Lama ◽  
Estefanía Otero-García ◽  
Alberto Bugarín

Author(s):  
Michel Gagnon ◽  
Amal Zouaq ◽  
Francisco Aranha ◽  
Faezeh Ensan ◽  
Ludovic Jean Louis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Arnaldo Pereira ◽  
Rui Pedro Lopes ◽  
José Luís Oliveira

The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findable, Accessible, Interoperable, and Reusable, and they are gaining traction in data stewardship. However, to explore their full potential, we must be able to transform legacy solutions smoothly into the FAIR Data ecosystem. In this paper, we introduce SCALEUS-FD, a FAIR Data extension of a legacy semantic web tool successfully used for data integration and semantic annotation and enrichment. The core functionalities of the solution follow the Semantic Web and Linked Data principles, offering a FAIR REST API for machine-to-machine operations. We applied a set of metrics to evaluate its “FAIRness” and created an application scenario in the rare diseases domain.


2016 ◽  
Vol 21 (16) ◽  
pp. 4557-4570 ◽  
Author(s):  
Beniamino Di Martino ◽  
Giuseppina Cretella ◽  
Antonio Esposito

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