摘要
采用马尔可夫回应模型 (MRM)和连续时间马尔可夫链 (CTMC)描述的随机过程Z(t) ,利用网络累积性能等参数的双拉普拉斯变换表达式将面向系统分析和面向对象分析的两种观点有机地结合在一起 ,该模型能够计算和预测网络的多种性能参数 ,为解决复杂的迭代次数计算 ,推导出了近似算法 ,最后将网络累积性能算法应用到一个简单网络子网中并进行了详细的数值分析·
By using Markov Reward Model (MRM) and Continuous Time Markov Chain (CTMC) Stochastic Process Z(t) was described. The oriented system viewpoints and the oriented user viewpoint were unified by network accumulate performance parameter's double Laplace transform expression. The model can be used to calculate and forecast several network's performance parameters. In order to solve complex iterative time's calculation process, an approximation of iterative time's algorithm derived out. The numerical value of network accumulate performance was calculated and analyzed in a sample network topology structure.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2002年第2期111-114,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目 (69973 0 11)
关键词
网络累积性能
马尔可夫回应模型
连续时间马尔可夫链
分布式网络
网络管理
性能管理
数值分析
network accumulate performance
network accumulate performance completion time
network performance reliability
network performance availability
Markov Reward Model
Continues Time Markov Chain
distributed network
network management
