QoS-Driven Grid Resource Selection Based on Novel Neural Networks

Author(s):  
Xianwen Hao ◽  
Yu Dai ◽  
Bin Zhang ◽  
Tingwei Chen ◽  
Lei Yang
2017 ◽  
Vol 26 (1) ◽  
pp. 169-184 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Nneoma A. Okoroafor ◽  
Seyed M. Buhari ◽  
Marc E. Frincu ◽  
Sahalu B. Junaidu

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.


2008 ◽  
pp. 195-206
Author(s):  
Yonatan Zetuny ◽  
Gabor Terstyanszky ◽  
Stephen Winter ◽  
Peter Kacsuk

Author(s):  
Adil Yousif ◽  
Abdul Hanan Abdullah ◽  
Mohammed Bakri Bashir

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