利用应用程序特征识别降低网格监控负载的研究  

Reducing the Grid Monitoring Workload Using Application Characteristics Identification

作　　者：吴众欣[1] 王克[2] 钱德沛[1,2] 

机构地区：[1]西安交通大学电子与信息工程学院,西安710049 [2]北京航空航天大学计算机学院,北京100191

出　　处：《西安交通大学学报》2009年第8期11-16,共6页Journal of Xi'an Jiaotong University

基　　金：国家高技术研究发展计划资助项目(2006AA01A118;2009AA01Z144);科技部国际合作项目(2006DFA11080)

摘　　要：针对网络监控负载随着监控任务的增加而增加，从而影响集群有效管理、利用及监控系统性能的问题，展开了利用应用程序特征识别降低网格监控负载的研究．采用主成分分析法分析程序执行过程中的监测数据，得到应用程序的主成分因子，再经由4种密集型基准程序构造的比对特征集合匹配，识别应用程序的主要特性，以此达到降低监控负载的目的．在原型系统上的应用程序特征识别及监控负栽评估实验表明，对于混合类型应用程序或某种密集型应用程序，监控负载可以减少20％～60％，对于巨大的采集数据，可以做到有效精炼．The overhead of monitoring that increases with the increasing monitoring tasks is crucial to the effective management and efficient utilization of the cluster computers and to the performance of the monitoring system. Researches are made to reduce the overhead of monitoring by identifying the main characteristics of the application. Main factors of the application are dynamically identified by performing principal component analysis （PCA） on the fly of application execution. The set of the main characteristics is identified through matching up with the comparison characteristic set that is created by 4 categories resource intensive benchmark so that the monitoring workload is reduced. A prototype monitoring system adopting the proposed strategy is implemented. Experimental results show that the monitoring workload is decreased by 20% to 60% in hybrid applications or some resource intensive applications. Large collected data can be effectively refined and processed before inputting it to a monitoring system.

关 键 词：网格监控 监控负载 主成分分析 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]