Title :
Future Clients´ Requests Estimation for Dynamic Resource Allocation in Cloud Data Center Using CGPANN
Author :
Ali, Jalil ; Zafari, Faheem ; Khan, Gul Muhammad ; Mahmud, Sahibzada Ali
Author_Institution :
Dept. of Electr. Eng., Univ. of Eng. & Technol., Peshawar, Pakistan
Abstract :
Cloud computing is an emerging and rapid growing field of Infrastructure as a Service (IaaS), it has to deal with resource allocation and power management issues. This paper proposes CGPANN to accurately forecast the client´s requests for a very short term duration of 1 second. A forecasting accuracy as high as 99.81% has been attained that verifies the accuracy of the proposed model. The experimental results show that the model outperforms all the contemporary models proposed in past.
Keywords :
cloud computing; computer centres; genetic algorithms; neural nets; power aware computing; resource allocation; telecommunication power management; CGPANN; Cartesian genetic programming evolved artificial neural network; IaaS; client request estimation; cloud computing; cloud data center; dynamic resource allocation; forecasting accuracy; infrastructure as a service; power management issues; resource allocation; Cloud computing; Computational modeling; Data models; Forecasting; Neural networks; Predictive models; Resource management; CGPANN; Cartesian Genetic Programming; Cloud Computing; Data center traffic forecasting; Dynamic Resource Allocation;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2013 12th International Conference on
Conference_Location :
Miami, FL
DOI :
10.1109/ICMLA.2013.189
