Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potential...Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine(SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance.In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.展开更多
Since the raising of the cloud computing, the applications of web service have been extended rapidly. However, the data centers of cloud computing also cause the problem of power consumption and the resources usually ...Since the raising of the cloud computing, the applications of web service have been extended rapidly. However, the data centers of cloud computing also cause the problem of power consumption and the resources usually have not been used effectively. Decreasing the power consumption and enhancing resource utilization become main issues in cloud computing environment. In this paper, we propose a method, called MBFDP （modified best fit decreasing packing）, to decrease power consumption and enhance resource utilization of cloud computing servers. From the results of experiments, the proposed solution can reduce power consumption effectively and enhance the utilization of resources of servers.展开更多
基金supported by the National Natural Science Foundation of China(61472005,61201252)CERNET Innovation Project(NGII20160207)
文摘Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine(SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance.In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.
文摘Since the raising of the cloud computing, the applications of web service have been extended rapidly. However, the data centers of cloud computing also cause the problem of power consumption and the resources usually have not been used effectively. Decreasing the power consumption and enhancing resource utilization become main issues in cloud computing environment. In this paper, we propose a method, called MBFDP （modified best fit decreasing packing）, to decrease power consumption and enhance resource utilization of cloud computing servers. From the results of experiments, the proposed solution can reduce power consumption effectively and enhance the utilization of resources of servers.
