Title :
Research on the Key technologies of Simulation Grid Resource Scheduling Based on Machine Learning
Author :
Xu, Xiaoming ; Yan, Xuefeng ; Ding, Qiuling
Author_Institution :
Coll. of Inf. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing
Abstract :
According to characteristics of simulation grid resources (SGR), an extend Web service description language (WSDL) was adopted to describe the attributes of SGRs, in order to facilitate the application of machine learning algorithms for SGR scheduling on a centralized-distributed SGR management model. By analyzing the specific requirements of distributed interactive simulation (DIS) task, a SGR scheduling model based on machine learning was proposed. Support vector machine (SVM) and incremental SVM were applied to implement SGRs classification when the features vectors were extracted from the WSDL documents. Scheduling agents can then carried out the SGR scheduling on classified SGRs. Experiments showed that the scheduling model can get federation overall optimal result with better performance.
Keywords :
Web services; grid computing; learning (artificial intelligence); scheduling; support vector machines; Web service description language; centralized-distributed management model; distributed interactive simulation; machine learning algorithms; simulation grid resource scheduling; support vector machine; Aerospace simulation; Computational modeling; Environmental economics; Machine learning; Machine learning algorithms; Resource management; Space technology; Support vector machine classification; Support vector machines; Technology management;
Conference_Titel :
Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-3893-8
Electronic_ISBN :
978-1-4244-3894-5
DOI :
10.1109/IWISA.2009.5073088