摘要
我们周围充满了各种网络;按照相似的内在机理,可以将它们分为物理网络和信息网络。对于具有明显物理特征的网络,我们可以运用物理常识解释其内部结构或节点的性质;而对于信息网络,我们往往需要结合一些先验知识去理解,社交网络正是这样一个例子。然而,对于那些并非具有显著物理或社交背景的网络,以往并没有明确的分析思路和方法。该文将尝试运用类似于分析社交网络的方法去分析电信CSB业务系统服务器集群上的进程网络;具体地预测进程网络中节点的崩溃(故障)状态。在这个特定的进程网络上,这种建模和分析思路得到了较为可信的结果;研究表明,进程节点的运行信息(如CPU和内存使用率)、进程间的通信情况以及进程节点在整个网络中的结构特征对于判断该节点的状态具有一定的指导价值,而上述特征在时间维度上的变化量同样反映了进程/端口的状态。
Various networks surrounding us can be divided into physical networks and information networks according to similar internal mechanisms.For networks with obvious physical characteristics,we can use the basic physical knowledge to explain the nature of its internal structure or nodes;For information networks,we may need to combine some prior knowledge to understand,and social network is such an example.However,there＇re no clear ways or means for analyzing networks without significant physical or social backgrounds.In this paper,we explore a similar approach of social network analysis to analyze the process network on China Telecom CSB cluster;specifically,to predict the crashing of process on the cluster.Such approach has brought credible results on this particular dataset,and according to our research,the running information such as loads of CPU and memory,communications between processes and the structural features in the process network are valuable in predicting the states of processes and ports;furthermore,the changes of features mentioned above in the time dimension reflect the states of processes or ports.
作者
程自强
黄荣
杨洋
CHENG Ziqiang;HUANG Rong;YANG Yang(College of Computer Science and Technology,Zhejiang University,Hangzhou,Zhejiang 310058,China;Shanghai Seahigh Telecom Corp.,Shanghai 201612,China)
出处
《中文信息学报》
CSCD
北大核心
2018年第8期103-110,共8页
Journal of Chinese Information Processing
基金
国家自然科学基金(61702447)
关键词
故障检测
社交网络
统计学习
二分类
fault detection
social network
statistical learning
binary classification
