DocumentCode :
2995738
Title :
A new hybrid model for request rate prediction in mobile cloud computing
Author :
Barati, Masoud ; Sharifian, Saeed
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol. Tehran Polytech., Tehran, Iran
fYear :
2015
fDate :
10-14 May 2015
Firstpage :
775
Lastpage :
780
Abstract :
One of the key characteristics of mobile cloud computing compared to the traditional systems is its dynamicity that requires a scalable resource allocation scheme. Since the preparation of a new virtual machine in cloud is a time consuming task, the cloud provider must predict the resources that will request by users in near future. In this paper, we proposed an intelligent hybrid model that predicts the upcoming resource demands. The model uses neural networks and GARCH as statistical model to achieve high accuracy in demand prediction. Our hybrid model shows better results compared to the rival methods according to standard metrics.
Keywords :
autoregressive processes; cloud computing; feedforward neural nets; mobile computing; resource allocation; virtual machines; (FFNN); GARCH; cloud provider; feedforward neural network; generalized autoregressive conditional heteroskedasticity model; intelligent hybrid model; mobile cloud computing; request rate prediction; resource demand prediction; scalable resource allocation scheme; statistical model; system dynamicity; virtual machine; Conferences; Decision support systems; Electrical engineering; GARCH; demand forecasting; mobile cloud computing; neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering (ICEE), 2015 23rd Iranian Conference on
Conference_Location :
Tehran
Print_ISBN :
978-1-4799-1971-0
Type :
conf
DOI :
10.1109/IranianCEE.2015.7146318
Filename :
7146318
Link To Document :
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