원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.9 No.6
2016.06
pp.269-278
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Aiming at the problem of replica selection optimization in cloud storage load balancing technology, a new dynamic selection algorithm based on genetic algorithm(GASA in short ) is proposed. According to the principle of genetic algorithm, the model of dynamic selection strategy based on genetic algorithm is constructed, and then the key steps of the replica selection criteria and genetic algorithm are mapped, and then the optimal solution is obtained by using the probability equation. Lastly. simulation results from cloud test bed. which is based on Optorsim. show that GASA can reduce data access latency and bandwidth consumption. and effectively achieve cloud load balancing between storage nodes and improve the speed of data access.
목차
Abstract
1. Introduction
2. Genetic Algorithm
3. Dynamic Prediction Selection Algorithm of Cloud Storage
3.1. Chromosome Coding
3.2. Calculation of Fitness Function
3.3. Pheromone Generation
3.4. Setting and Selection Strategy
3.5. Algorithm Flow
4. Algorithm Simulation Experiment and Result Analysis
4.1. Experimental Environment
4.2. Analysis of Experimental Results
5. Conclusions
Acknowledgements
References
1. Introduction
2. Genetic Algorithm
3. Dynamic Prediction Selection Algorithm of Cloud Storage
3.1. Chromosome Coding
3.2. Calculation of Fitness Function
3.3. Pheromone Generation
3.4. Setting and Selection Strategy
3.5. Algorithm Flow
4. Algorithm Simulation Experiment and Result Analysis
4.1. Experimental Environment
4.2. Analysis of Experimental Results
5. Conclusions
Acknowledgements
References
저자정보
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