Data resource discovery model based on hybrid architecture in data grid environment

2014 ◽  
Vol 27 (3) ◽  
pp. 507-525 ◽  
Author(s):  
Tinghuai Ma ◽  
Yinhua Lu ◽  
Sunyuan Shi ◽  
Wei Tian ◽  
Xin Wang ◽  
...  
2017 ◽  
Vol 7 (1) ◽  
pp. 1398-1404
Author(s):  
M. Mollamotalebi ◽  
R. Maghami ◽  
A. S. Ismail

Grid computing environments include heterogeneous resources shared by a large number of computers to handle data and process intensive applications. The required resources must be accessible for the grid applications on demand, which makes resource discovery a critical service. In recent years, different techniques are provided to index and discover grid resources. Response time and message load during the search process highly affect the efficiency of resource discovery. This paper proposes a technique to forward the queries based on the resource types accessible through each neighbor in super-peer-based grid resource discovery approaches. The proposed technique is simulated in GridSim and the experimental results indicated that it is able to reduce the response time and message load during the search process especially when the grid environment contains a large number of nodes.


Author(s):  
Sajindra Jayasena ◽  
Chin-Peng Yee ◽  
Jie Song ◽  
Abele Stoelwinder ◽  
Chong Wee See ◽  
...  

Author(s):  
Eleana Asimakopoulou ◽  
Chimay J. Anumba ◽  
Bouchlaghem ◽  
Bouchlaghem

Much work is under way within the Grid technology community on issues associated with the development of services to foster collaboration via the integration and exploitation of multiple autonomous, distributed data sources through a seamless and flexible virtualized interface. However, several obstacles arise in the design and implementation of such services. A notable obstacle, namely how clients within a data Grid environment can be kept automatically informed of the latest and relevant changes about data entered/committed in single or multiple autonomous distributed datasets is identified. The view is that keeping interested users informed of relevant changes occurring across their domain of interest will enlarge their decision-making space which in turn will increase the opportunities for a more informed decision to be encountered. With this in mind, the chapter goes on to describe in detail the model architecture and its implementation to keep interested users informed automatically about relevant up-to-date data.


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