CAJ | 학술논문

在MDS4监控模型的基础上, 设计了基于可靠存储与容侵数据网格的监控模型, 分析了监控模型中计算资源的负载特性、指标. 然后, 设计了基于SVR的时间序列自回归预测模型, 提出了用于数据网格负载预测的监控ARSVR方法. 最后, 利用AR模型对历史观测序列进行建模, 确定模型的阶次. 根据历史数据对SVR进行训练, 得到回归函数. 仿真实验结果表明, ARSVR方法能对节点的负载进行有效预测.
재MDS4감공모형적기출상, 설계료기우가고존저여용침수거망격적감공모형, 분석료감공모형중계산자원적부재특성、지표. 연후, 설계료기우SVR적시간서렬자회귀예측모형, 제출료용우수거망격부재예측적감공ARSVR방법. 최후, 이용AR모형대역사관측서렬진행건모, 학정모형적계차. 근거역사수거대SVR진행훈련, 득도회귀함수. 방진실험결과표명, ARSVR방법능대절점적부재진행유효예측.
Based on the monitoring and discovery service 4(MDS4)model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then,a time-series autoregre-ssive prediction model is devised. And an autoregressive support vector regression(ARSVR)monitoring method is put forward to predict the node load of the data grid.Finally,a model for histo-rical observations sequences is set up using the autoregressive (AR)model and the model order is determined.The support vector regression(SVR)model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.