Design and Implementation of a Time Prediction Model for LS-DYNA Cloud Computing Platform

Author(s):  
Wenqian Zhang ◽  
Qingzheng Xu ◽  
Mingjun Zhao ◽  
Jianhang Zhang ◽  
Na Wang
2014 ◽  
Vol 651-653 ◽  
pp. 2028-2031
Author(s):  
Yan Jun An ◽  
Yue Hou

At present, the facial recognition is largely using in the criminal investigation for the identification of suspects, the information from the informants, however, is very hard to obtain the main feature and to draw it accurately because of the affection of mood, light, visual differences and so on. However, with some computer technology, combining the resource from big data and cloud computing platform, the success rate of the search and recognition could be greatly improved. Therefore this article will bring out a facial drawing system based on big data and cloud computing platform.


2013 ◽  
Vol 303-306 ◽  
pp. 2235-2240 ◽  
Author(s):  
Wei Xiao ◽  
Chun Lei Ji ◽  
Jian Dun Li

Considering the low efficiency of massive data retrieving in traditional parallel processing, by taking advantage of the great availability of cloud computing paradigm, we propose a hybrid solution based on Map-Reduce model and distributed computing framework--Spark. Moreover, we design and implement this solution in our lab. The results show that the solution can effectively improve the performance of massive data retrieving.


2014 ◽  
Vol 631-632 ◽  
pp. 210-217 ◽  
Author(s):  
Jian Jun Jiang ◽  
Zhi Feng Ding

Cloud computing model, based on cloud technologies, is an evolution to traditional computing model. It fully separates device, operating system, application and user data from each other while freely combines these components to form different computing paths. In cloud computing platform, storage cloud, desktop cloud and application cloud are built in the backend, while user data, operating systems, and applications are deployed in the data center. Therefore, user only needs to focus on data, and is able to access, process and share data with any device, from anywhere and at anytime.


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