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
보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Grid and Distributed Computing
간기
격월간
pISSN
2005-4262
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.9 No.6