Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology (Beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Baidu Online Network Technology Co., Ltd.

Xianping Qu

Bo Wang

Yu Chen

Yun Chen

Beibei Miao

Dong Wang

Shimin Tao

Zhi Zang

Xuanyou Guo

Hui Su

Ping Liu

Jia He

Yongqian Sun

Yangsha Wan

Xuebo Jin

A method and apparatus for monitoring a system are provided. The method includes: acquiring a series of historical status index data of a monitored system during at least one data collection period; introducing the series of historical status index data into a pre-trained recommended feature extraction model to perform a matching operation, to obtain a feature extraction algorithm matching the series of historical status index data as a recommended feature extraction algorithm; determining a normal value range of feature values obtained by performing feature extraction on the series of status index data of the monitored system according to the recommended feature extraction algorithm; and monitoring the monitored system according to the recommended feature extraction algorithm and the normal value range.

Method and apparatus for monitoring system

A method, an apparatus and a storage medium for diagnosing failure based on a service monitoring indicator are provided. Service monitoring indicator of a server is analyzed to obtain a degree of abnormal fluctuation in the service monitoring indicator. Servers with similar degrees of abnormal fluctuation are clustered according to the degree of abnormal fluctuation of the service monitoring indicator, to obtain clustered results that include the servers and the service monitoring indicator. A location where the service fails is determined according to the clustered results.

Method, apparatus, and storage medium for diagnosing failure based on a service monitoring indicator of a server by clustering servers with similar degrees of abnormal fluctuation

In one embodiment, a Kalman filter based capacity forecasting method includes acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.

Kalman filter based capacity forecasting method, system and computer equipment

Embodiments of the present disclosure provide a disk capacity predicting method, apparatus, equipment and non-volatile computer storage medium. On the one hand, the change data of the disk capacity is obtained according to the historical capacity data of the disk; then the target inflection point in the historical capacity data is obtained according to the change data of the disk capacity; and then the linear relationship between the time and disk capacity is obtained according to the historical capacity data after the target inflection point. Therefore, technical solutions provided by embodiments of the present disclosure may achieve improvement of accuracy of prediction of disk capacity trend and reduce the cost needed in disk capacity prediction.

Disk capacity predicting method, apparatus, equipment and non-volatile computer storage medium

A system monitoring method and apparatus comprises: collecting periodically status indicator data of a monitored system to generate a status indicator data sequence; selecting predetermined pieces of status indicator data according to data collecting time in a reverse chronological order; determining a category from predetermined categories, the predetermined pieces of status indicator data belonging to the determined category; selecting, from the historical status indicator data, status indicator data belonging to the determined category and obtained in a collection period as characteristic data of the determined category; calculating a predicted value of a status indicator of the system at a predicting moment using the characteristic data; and determining whether the system is abnormal, based on a difference between the calculated predicted value and a true value of the status indicator of the system collected at the predicting moment. The present implementation can accurately find the abnormality of the system rapidly.

System monitoring method and apparatus

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Beijing, China

Xianping Qu