摘要
针对网络流量监测点最优部署(Optimal Deployment of Flow Monitors,ODFM)问题,提出了ODFM问题的通用模型。将遗传算法和模拟退火算法相结合,给出了求解ODFM问题的遗传模拟退火算法(GA-SA)。通过仿真实验,将GA-SA和标准遗传算法(Standard Genetic Algorithm,SGA)以及Suh等人提出的两步近似算法(Two-Stage Approximation Algorithm,TSAA)的求解性能进行了比较。实验结果表明,与SGA和TSAA相比,GA-SA可获得15%以上的求解性能提升。
To address the problem of Optimal Deployment of Flow Monitors （ODFM） in large IP networks, a universal model of the ODFM problem was proposed. Meanwhile, a genetic simulated annealing algorithm （GA-SA） was given to solve it, The performance of GA-SA was compared with both Standard Genetic Algorithm （SGA） and the Two-Stage Approximation Algorithm （TSAA） introduced by Suh, et al, through simulation. The experimental results show that GA-SA outperforms both SGA and TSAA by up to 15% in terms of qualitv of solutions.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2008年第9期2475-2478,2482,共5页
Journal of System Simulation
基金
国家863计划项目:大规模接入汇聚路由器系统性能及关键技术研究(2004AA103130)
关键词
流量监测
优化部署
混合遗传算法
模拟退火
flow monitoring
optimal deployment
hybrid genetic algorithm
simulated annealing
