Granted Patent B2

US 12,399,795 · App. 18/265,959 · Granted Aug 26, 2025

Inventors: Xiangsheng Kong (Shandong, CN); Hui Wang (Shandong, CN); Xianzhuang Li (Shandong, CN)

Assignee: INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD.

G06F11/3051G06F11/3072

Abstract

Claims (48)

1. A large-scale K8s cluster monitoring method, comprising:

performing classification on data sources in a cluster on a basis of a monitoring metric configuration means, and creating a data list on a basis of the data sources and the monitoring metric configuration means corresponding to the data sources;

in response to receiving a command to monitor a first monitoring metric set of a first data source, obtaining a monitoring metric configuration means corresponding to the first data source on a basis of the data list;

performing configuration on a monitoring metric on a basis of the first monitoring metric set and by means of the obtained monitoring metric configuration means; and

obtaining from the first data source a monitoring metric in the first monitoring metric set every preset amount of time, and performing monitoring on the monitoring metric;

wherein the performing classification on data sources in a cluster on a basis of a monitoring metric configuration means comprises:

classifying the data sources in the cluster into a first type of data source and a second type of data source, wherein the first type of data source prohibits the monitoring metric by modifying a startup parameter, and the second type of data source prohibits the monitoring metric by modifying a configuration file;

wherein the performing configuration on a monitoring metric on the basis of the first monitoring metric set and by means of the obtained monitoring metric configuration means comprises:

prohibiting a monitoring metric not in the first monitoring metric set by means of the monitoring metric configuration means.

2. The large-scale K8s cluster monitoring method according to claim 1 , wherein the first monitoring metric set comprises at least one of: CPU usage, memory usage, disk usage, disk read/write rate, and network packet loss rate.

3. The large-scale K8s cluster monitoring method according to claim 1 , wherein the obtaining from the first data source a monitoring metric in the first monitoring metric set every preset amount of time, and performing monitoring on the monitoring metric comprises:

obtaining from the first data source the monitoring metric in the first monitoring metric set every preset amount of time, and sending the monitoring metric to a system monitoring and alarm system for monitoring; and

in response to the monitoring metric being abnormal, giving an alarm by the system monitoring and alarm system.

4. The large-scale K8s cluster monitoring method according to claim 3 , wherein the system monitoring and alarm system comprises prometheus.

5. The large-scale K8s cluster monitoring method according to claim 1 , wherein the data sources comprises: kubelet configured for providing Pod's monitoring data, node-exporter configured for providing node information data, and kube-metric configured for providing resource data.

6. The large-scale K8s cluster monitoring method according to claim 1 , wherein the first type of data source is node-exporter; and

modified startup parameters comprise --no-collector.wifi that means not to collect wifi-related monitoring data, --no-collector.meminfo that means not to collect memory-related monitoring data, and --no-collector.cpu means not to collect CPU-related monitoring data.

7. The large-scale K8s cluster monitoring method according to claim 1 , wherein the second type of data source is kubelet; and

modified configuration files comprise cpu.disabled=true that means to disable cpu information collection, mem.disabled=true that means to disable memory information collection, and net.disabled=true that means to disable network information collection.

8. A computer device, comprising:

at least one processor; and

a memory storing computer instructions executable on the processor, wherein when executed by the processor, the instructions implement operations comprising:

performing classification on data sources in a cluster on a basis of a monitoring metric configuration means, and creating a data list on a basis of the data sources and the monitoring metric configuration means corresponding to the data sources;

in response to receiving a command to monitor a first monitoring metric set of a first data source, obtaining a monitoring metric configuration means corresponding to the first data source on a basis of the data list;

performing configuration on a monitoring metric on a basis of the first monitoring metric set and by means of the obtained monitoring metric configuration means; and

obtaining from the first data source a monitoring metric in the first monitoring metric set every preset amount of time, and performing monitoring on the monitoring metric;

wherein the performing classification on data sources in a cluster on a basis of a monitoring metric configuration means comprises:

classifying the data sources in the cluster into a first type of data source and a second type of data source, wherein the first type of data source prohibits the monitoring metric by modifying a startup parameter, and the second type of data source prohibits the monitoring metric by modifying a configuration file;

wherein the performing configuration on a monitoring metric on the basis of the first monitoring metric set and by means of the obtained monitoring metric configuration means comprises:

prohibiting a monitoring metric not in the first monitoring metric set by means of the monitoring metric configuration means.

9. The computer device according to claim 8 , wherein the first monitoring metric set comprises at least one of: CPU usage, memory usage, disk usage, disk read/write rate, and network packet loss rate.

10. The computer device according to claim 8 , wherein the obtaining from the first data source a monitoring metric in the first monitoring metric set every preset amount of time, and performing monitoring on the monitoring metric comprises:

obtaining from the first data source the monitoring metric in the first monitoring metric set every preset amount of time, and sending the monitoring metric to a system monitoring and alarm system for monitoring; and

in response to the monitoring metric being abnormal, giving an alarm by the system monitoring and alarm system.

11. The computer device according to claim 10 , wherein the system monitoring and alarm system comprises prometheus.

12. A non-transitory computer-readable storage medium, storing a computer program, wherein when executed by a processor, the computer program implements operations comprising:

performing classification on data sources in a cluster on a basis of a monitoring metric configuration means, and creating a data list on a basis of the data sources and the monitoring metric configuration means corresponding to the data sources;

in response to receiving a command to monitor a first monitoring metric set of a first data source, obtaining a monitoring metric configuration means corresponding to the first data source on a basis of the data list;

performing configuration on a monitoring metric on a basis of the first monitoring metric set and by means of the obtained monitoring metric configuration means; and

obtaining from the first data source a monitoring metric in the first monitoring metric set every preset amount of time, and performing monitoring on the monitoring metric;

wherein the performing classification on data sources in a cluster on a basis of a monitoring metric configuration means comprises:

classifying the data sources in the cluster into a first type of data source and a second type of data source, wherein the first type of data source prohibits the monitoring metric by modifying a startup parameter, and the second type of data source prohibits the monitoring metric by modifying a configuration file;

wherein the performing configuration on a monitoring metric on the basis of the first monitoring metric set and by means of the obtained monitoring metric configuration means comprises:

prohibiting a monitoring metric not in the first monitoring metric set by means of the monitoring metric configuration means.

13. The non-transitory computer-readable storage medium according to claim 12 , wherein the first monitoring metric set comprises at least one of: CPU usage, memory usage, disk usage, disk read/write rate, and network packet loss rate.

14. The non-transitory computer-readable storage medium according to claim 12 , wherein the obtaining from the first data source a monitoring metric in the first monitoring metric set every preset amount of time, and performing monitoring on the monitoring metric comprises:

obtaining from the first data source the monitoring metric in the first monitoring metric set every preset amount of time, and sending the monitoring metric to a system monitoring and alarm system for monitoring; and

in response to the monitoring metric being abnormal, giving an alarm by the system monitoring and alarm system.

Assignments (2)

Priority Claims (1)

Continuity (1)

References Cited (10)