基于马尔科夫链自适应能力任务分派系统设计  

An Assignment Method Base In Markov Chains For Distributed Systems

作　　者：谢伟鹏[1] 周家纪[1] 

机构地区：[1]成都理工大学信息工程学院

出　　处：《微计算机信息》2007年第3期234-235,共2页Control & Automation

摘　　要：当分布式系统中各处理结点相互完全独立,并且在对其他结点的状态不是很了解情况下,利用组合数学和随机过程的方法提出一种机制,该机制能使各结点在没有全局管理者的参与下,仅根据系统运行的反馈决定下一步的任务,从而使整个系统的任务分配达到最优。该方法能很好地处理最普遍的任务分派问题。In a distributed system without a central administrator ,suppose all independent processing units do not know the condition of one another ,a new system is designed to undertake the tasks in satisfy proportions to cooperate for the best results,in which only self-learning is used.This paper also analyzes the performance of this system by Markov chains,and presents a robust method of self-learning for independent processing units in this kind of system.The method can be used to solve the general assignment problem.

关 键 词：独立结点 任务分配 自我优化 马尔科夫链 算法 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]