원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.6
2015.06
pp.299-308
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Properly allocation of virtual machines is important for computing infrastructures scheduling. This paper presents systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory. According to request service data from users to provide proper VMs roughly via intelligent pattern recognition based on RBFNN, the data is sent to a multiple-targets optimization process to produce VMs allocation matrix precisely, thus enable to minimize the cast and enhance efficiency of the whole array to achieve low consumption optimization and ensure the stability of the system. Simulation experiments confirmed the effectiveness of this model and adaption ability in online dynamics.
목차
Abstract
1. Introduction
2. Preliminary Results
2.1. Energy Consumption of VMs
2.2. Matrix Expression of Feedback Control System
2.3. Radial Basis Function Neural Networks (RBFNN)
3. Optimization Modeling
3.1. State Space Expression of VMs
3.2. Learning Mechanism of RBFNN
3.3. Energy Consumption Model: Multiple-target Function
4. Model Simulation
6. Conclusions
7. Related Works
Acknowledgements
References
1. Introduction
2. Preliminary Results
2.1. Energy Consumption of VMs
2.2. Matrix Expression of Feedback Control System
2.3. Radial Basis Function Neural Networks (RBFNN)
3. Optimization Modeling
3.1. State Space Expression of VMs
3.2. Learning Mechanism of RBFNN
3.3. Energy Consumption Model: Multiple-target Function
4. Model Simulation
6. Conclusions
7. Related Works
Acknowledgements
References
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자료제공 : 네이버학술정보