【摘要】 为更快地计算大尺度复杂网络结构的相关参数,设计并实现一种松耦合分布式计算框架。将分散于网络中的松耦合计算节点汇集起来,通过任务队列使各计算节点共同参与复杂网络的相关分布式计算,并能随时加入或者退出计算,利用分散于网络中松耦合的计算节点提高复杂网络相关分析的计算速度。基于该框架,实现对大尺度复杂网络的平均最短路径长度、网络直径和网络效率的分布式计算。实验结果表明,在保证计算结果正确的前提下,该框架可充分利用网络中闲散的计算资源,提高运算效率。更多还原

【Abstract】 In order to calculate the topological characteristics of large-scale complex networks faster,a distributed computing framework for analyzing large-scale complex networks is designed and implemented in this paper.It collects loose coupling computing nodes distributed in the local network or the Internet.By using a task queue,computing nodes can join or quit during computing at any time.By fully leveraging the loose coupling computing resources distributed in a network,the framework makes the speed of analyzing large-scale complex networks enhanced greatly.Based on this framework,the distributed computing of average length of the shortest paths and the efficiency of large-scale complex networks is implemented.Experimental results show that this framework can make full use of the idle computing resources in the network,and greatly improves the computing performance,on the premise of ensuring the correctness of computational results.更多还原