Tencent Cloud CVM¶
Use the "Cloud Sync" series of script packages in the script market to synchronize cloud monitoring and cloud asset data to TrueWatch
Configuration¶
Install Func¶
It is recommended to activate the TrueWatch Integration - Extension - DataFlux Func (Automata): all prerequisites are automatically installed, please proceed with the script installation.
If deploying Func manually, refer to Manual Func Deployment
Install Script¶
Note: Please prepare the required Tencent Cloud AK in advance (for simplicity, you can directly grant the global read-only permission
ReadOnlyAccess
).
To synchronize CVM monitoring data, we install the corresponding collection script: "TrueWatch Integration (Tencent Cloud - CVM Collection)" (ID: integration_tencentcloud_cvm
).
Click [Install], then enter the corresponding parameters: Tencent Cloud AK, Tencent Cloud account name.
Click [Deploy Startup Script], the system will automatically create a Startup
script set and configure the corresponding startup script.
After enabling, you can see the corresponding automatic trigger configuration in "Management / Automatic Trigger Configuration". Click [Execute] to immediately execute once without waiting for the scheduled time. Wait a moment, then you can view the execution task records and corresponding logs.
To collect corresponding logs, you also need to enable the corresponding log collection script. To collect bills, you need to enable the cloud bill collection script.
We have collected some configurations by default, see the Metrics section for details.
Configure Custom Cloud Object Metrics
Verification¶
- In "Management / Automatic Trigger Configuration", confirm whether the corresponding task exists in the automatic trigger configuration, and check the task records and logs for any exceptions.
- In TrueWatch, check if asset information exists in "Infrastructure / Custom".
- In TrueWatch, check if there is corresponding monitoring data in "Metrics".
Metrics¶
After configuring Tencent Cloud Cloud Monitor, the default Measurement is as follows. More metrics can be collected through configuration Tencent Cloud Cloud Monitor Metrics Details
CPU Monitoring¶
Metric Name | Metric Description | Description | Unit | Dimension | Statistical Granularity |
---|---|---|---|---|---|
CpuUsage |
CPU Utilization | Real-time CPU usage percentage during machine operation | % | InstanceId |
10s、60s、300s、3600s、86400s |
CpuLoadavg |
CPU 1-minute Average Load | Average number of tasks using and waiting to use CPU in 1 minute (not available for Windows machines) | - | InstanceId |
10s、60s、300s、3600s、86400s |
Cpuloadavg5m |
CPU 5-minute Average Load | Average number of tasks using and waiting to use CPU in 5 minutes (not available for Windows machines) | - | InstanceId |
60s、300s、3600s |
Cpuloadavg15m |
CPU 15-minute Average Load | Average number of tasks using and waiting to use CPU in 15 minutes (not available for Windows machines) | - | InstanceId |
60s、300s、3600s |
BaseCpuUsage |
Base CPU Usage Rate | Base CPU usage rate is collected and reported by the host machine, and data can be viewed without installing monitoring components. Data can still be collected and reported when the virtual machine is under high load. | % | InstanceId |
10s、60s、300s、3600s、86400s |
GPU Monitoring¶
Metric Name | Metric Description | Description | Unit | Dimension | Statistical Granularity |
---|---|---|---|---|---|
GpuMemTotal |
GPU Memory Total | GPU memory total | MB | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
GpuMemUsage |
GPU Memory Usage Rate | GPU memory usage rate | % | InstanceId |
10s、60s、300s、3600s、86400s |
GpuMemUsed |
GPU Memory Usage | Evaluate the memory usage of the load | MB | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
GpuPowDraw |
GPU Power Usage | GPU power usage | W | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
GpuPowLimit |
GPU Power Total | GPU power total | W | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
GpuPowUsage |
GPU Power Usage Rate | GPU power usage rate | % | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
GpuTemp |
GPU Temperature | Evaluate GPU cooling status | °C | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
GpuUtil |
GPU Utilization | Evaluate the computing power consumed by the load, the percentage of non-idle state | % | InstanceId |
10s、 60s、 300s、 3600s、 86400s |
Network Monitoring¶
Metric Name | Metric Description | Description | Unit | Dimension | Statistical Granularity |
---|---|---|---|---|---|
LanOuttraffic |
Internal Network Outbound Bandwidth | Average outbound traffic per second of the internal network card | Mbps | InstanceId |
10s、60s、300s、3600s、86400s |
LanIntraffic |
Internal Network Inbound Bandwidth | Average inbound traffic per second of the internal network card | Mbps | InstanceId |
10s、60s、300s、3600s、86400s |
LanOutpkg |
Internal Network Outbound Packet Rate | Average outbound packet rate per second of the internal network card | packets/sec | InstanceId |
10s、60s、300s、3600s、86400s |
LanInpkg |
Internal Network Inbound Packet Rate | Average inbound packet rate per second of the internal network card | packets/sec | InstanceId |
10s、60s、300s、3600s、86400s |
WanOuttraffic |
External Network Outbound Bandwidth | Average outbound traffic rate per second of the external network, the minimum granularity data is calculated as total outbound traffic in 10 seconds / 10 seconds. This data is the sum of outbound/inbound bandwidth of EIP+CLB+CVM | Mbps | InstanceId |
10s、60s、300s、3600s、86400s |
WanIntraffic |
External Network Inbound Bandwidth | Average inbound traffic rate per second of the external network, the minimum granularity data is calculated as total inbound traffic in 10 seconds / 10 seconds. This data is the sum of outbound/inbound bandwidth of EIP+CLB+CVM | Mbps | InstanceId |
10s、60s、300s、3600s、86400s |
WanOutpkg |
External Network Outbound Packet Rate | Average outbound packet rate per second of the external network card | packets/sec | InstanceId |
10s、60s、300s、3600s、86400s |
WanInpkg |
External Network Inbound Packet Rate | Average inbound packet rate per second of the external network card | packets/sec | InstanceId |
10s、60s、300s、3600s、86400s |
AccOuttraffic |
External Network Outbound Traffic | Average outbound traffic per second of the external network card | MB | InstanceId |
10s、60s、300s、3600s、86400s |
TcpCurrEstab |
TCP Connection Count | Number of TCP connections in ESTABLISHED state | count | InstanceId |
10s、60s、300s、3600s、86400s |
TimeOffset |
UTC Time and NTP Time Difference of the Virtual Machine | UTC time and NTP time difference of the virtual machine | seconds | InstanceId |
60s、300s、3600s、86400s |
Memory Monitoring¶
Metric Name | Metric Description | Description | Unit | Dimension | Statistical Granularity |
---|---|---|---|---|---|
MemUsed |
Memory Usage | Actual memory used by the user, excluding memory occupied by buffers and system cache. Total memory - available memory (including buffers and cached) to get the memory usage value, excluding buffers and cached | MB | InstanceId |
10s、60s、300s、3600s、86400s |
MemUsage |
Memory Utilization Rate | Actual memory utilization rate by the user, excluding memory occupied by buffers and system cache. Excluding cache, buffer, and remaining, the ratio of actual memory used by the user to total memory | % | InstanceId |
10s、60s、300s、3600s、86400s |
Disk Monitoring¶
Metric Name | Metric Description | Description | Unit | Dimension | Statistical Granularity |
---|---|---|---|---|---|
CvmDiskUsage |
Disk Utilization Rate | Percentage of disk used capacity to total capacity (all disks) | % | InstanceId |
60s、300s |
RDMA Monitoring¶
Metric Name | Metric Description | Metric Description (Optional) | Unit | Dimension | Statistical Granularity |
---|---|---|---|---|---|
RdmaIntraffic |
RDMA Network Card Receive Bandwidth | RDMA network card receive bandwidth | Mbps | InstanceId |
60s、 300s、 3600s、 86400s |
RdmaOuttraffic |
RDMA Network Card Send Bandwidth | RDMA network card send bandwidth | Mbps | InstanceId |
60s、 300s、 3600s、 86400s |
RdmaInpkt |
RDMA Network Card Inbound Packet Rate | RDMA network card inbound packet rate | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
RdmaOutpkt |
RDMA Network Card Outbound Packet Rate | RDMA network card outbound packet rate | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
CnpCount |
CNP Statistics | Congestion Notification Packet Statistics | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
EcnCount |
ECN Statistics | Explicit Congestion Notification Statistics | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
RdmaPktDiscard |
Endpoint Packet Loss Rate | Endpoint packet loss rate | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
RdmaOutOfSequence |
Receiver Out-of-Order Error Rate | Receiver out-of-order error rate | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
RdmaTimeoutCount |
Sender Timeout Error Rate | Sender timeout error rate | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
TxPfcCount |
TX PFC Statistics | TX PFC statistics | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
RxPfcCount |
RX PFC Statistics | RX PFC statistics | packets/sec | InstanceId |
60s、 300s、 3600s、 86400s |
Object¶
The collected Tencent Cloud CVM object data structure can be viewed in "Infrastructure - Custom".
{
"measurement": "tencentcloud_cvm",
"tags": {
"name" : "ins-bahxxxx",
"RegionId" : "ap-shanghai",
"Zone" : "ap-shanghai-1",
"InstanceId" : "ins-bahxxxx",
"InstanceChargeType": "POSTPAID_BY_HOUR",
"InstanceType" : "SA2.MEDIUM2",
"OsName" : "TencentOS Server 3.1 (TK4)"
},
"fields": {
"CPU" : 2,
"Memory" : 2,
"InstanceState" : "RUNNING",
"PublicIpAddresses" : "{Public IP Data}",
"PrivateIpAddresses": "{Private IP Data}",
"SystemDisk" : "{System Disk JSON Data}",
"DataDisks" : "{Data Disk JSON Data}",
"Placement" : "{Region JSON Data}",
"ExpiredTime" : "2022-05-07T01:51:38Z",
"message" : "{Instance JSON Data}"
}
}