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    HPA Metrics

    Last updated: 2023-03-30 18:26:22
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      Horizontal Pod Autoscaler (HPA) can automatically scale the number of Pods for services according to the average CPU utilization of target Pods and other metrics. You can set auto-scaling triggering metrics in the console including CPU, memory, disk, network, and GPU metrics. You can also use these metrics when creating and editing HPAs with YAML files. This document provides an example of configuring a YAML file.

      Autoscaling Metrics

      The following tables list the details of the autoscaling metrics:
      Note
      Each variable under metricName has its own unit which is listed in the default unit column. You can omit such units when compiling the YAML file.

      CPU metrics

      Metric Name
      in the Console
      Unit
      in the Console
      Remarks
      Type
      metricName
      Default Unit
      CPU Usage
      Core
      Number of CPU cores used by the Pod
      Pods
      k8s_pod_cpu_core_used
      Core
      CPU Utilization
      (per node)
      %
      Percentage of total CPU of the node used by the Pod
      Pods
      k8s_pod_rate_cpu_core_used_node
      %
      CPU Utilization
      (per Request)
      %
      Ratio of the total number of CPU cores used by the Pod and the value of Request specified by the container in the Pod
      Pods
      k8s_pod_rate_cpu_core_used_request
      %
      CPU Utilization
      (per Limit)
      %
      Ratio of the total number of CPU cores used by the Pod and the sum of Limit specified by the container in the Pod
      Pods
      k8s_pod_rate_cpu_core_used_limit
      %

      Disk metrics

      Metric Name
      in the Console
      Unit
      in the Console
      Remarks
      Type
      metricName
      Default Unit
      Disk Write Traffic
      KB/s
      Pod's disk write rate
      Pods
      k8s_pod_fs_write_bytes
      B/s
      Disk Read Traffic
      KB/s
      Pod's disk read rate
      Pods
      k8s_pod_fs_read_bytes
      B/s
      Disk Read IOPS
      Times/s
      Number of I/O times Pod reads data from disk
      Pods
      k8s_pod_fs_read_times
      Times/s
      Disk Write IOPS
      Times/s
      Number of I/O times Pod writes data to disk
      Pods
      k8s_pod_fs_write_times
      Times/s

      Network metrics

      Metric Name
      in the Console
      Unit
      in the Console
      Remarks
      Type
      metricName
      Default Unit
      Network bandwidth in
      Mbps
      Sum of inbound bandwidth of all containers per Pod
      Pods
      k8s_pod_network_receive_bytes_bw
      Bps
      Network bandwidth out
      Mbps
      Sum of outbound bandwidth of all containers per Pod
      Pods
      k8s_pod_network_transmit_bytes_bw
      Bps
      Network traffic in
      KB
      Sum of inbound traffic of all containers per Pod
      Pods
      k8s_pod_network_receive_bytes
      B
      Network traffic out
      KB
      Sum of outbound traffic of all containers per Pod
      Pods
      k8s_pod_network_transmit_bytes
      B
      Network packets in
      Count/s
      Sum of inbound packets of all containers per Pod
      Pods
      k8s_pod_network_receive_packets
      Count/s
      Network packets out
      Count/s
      Sum of outbound packets of all containers per Pod
      Pods
      k8s_pod_network_transmit_packets
      Count/s

      Memory metrics

      Metric Name
      in the Console
      Unit
      in the Console
      Remarks
      Type
      metricName
      Default Unit
      Memory Usage
      MiB
      Amount of memory used by the Pod
      Pods
      k8s_pod_mem_usage_bytes
      B
      Memory Usage (excluding cache)
      MiB
      Pod memory usage, excluding cache
      Pods
      k8s_pod_mem_no_cache_bytes
      B
      Memory Utilization (per node)
      %
      Percentage of total memory of the node used by the Pod
      Pods
      k8s_pod_rate_mem_usage_node
      %
      Memory Utilization (per node, excluding cache)
      %
      Percentage of total memory of the node used by the Pod, excluding cache
      Pods
      k8s_pod_rate_mem_no_cache_node
      %
      Memory Utilization (per Request)
      %
      Percentage of total memory of the Request used by the Pod
      Pods
      k8s_pod_rate_mem_usage_request
      %
      Memory Utilization (per Request, excluding cache)
      %
      Percentage of total memory of the Request used by the Pod, excluding cache
      Pods
      k8s_pod_rate_mem_no_cache_request
      %
      Memory Utilization (per Limit)
      %
      Percentage of Pod memory usage to the Limit value
      Pods
      k8s_pod_rate_mem_usage_limit
      %
      Memory Utilization (per Limit, excluding cache)
      %
      Percentage of Pod memory usage to the Limit value, excluding cache
      Pods
      k8s_pod_rate_mem_no_cache_limit
      %

      GPU

      Note
      The following GPU-related triggering metrics can only be used in TKE Serverless clusters.
      Metric Name
      in the Console
      Unit
      in the Console
      Remarks
      Type
      metricName
      Default Unit
      GPU Usage
      CUDA Core
      Pod GPU usage
      Pods
      k8s_pod_gpu_used
      CUDA Core
      GPU Applications
      CUDA Core
      Pod GPU applications
      Pods
      k8s_pod_gpu_request
      CUDA Core
      GPU Utilization (per Request)
      %
      Percentage of GPU usage to the Request value
      Pods
      k8s_pod_rate_gpu_used_request
      %
      GPU Utilization (per node)
      %
      GPU usage percentage in the node
      Pods
      k8s_pod_rate_gpu_used_node
      %
      GPU Memory Usage
      MiB
      Pod GPU memory usage
      Pods
      k8s_pod_gpu_memory_used_bytes
      B
      GPU Memory Applications
      MiB
      Pod GPU memory applications
      Pods
      k8s_pod_gpu_memory_request_bytes
      B
      GPU Memory Utilization (per Request)
      %
      Percentage of GPU memory usage to the Request value
      Pods
      k8s_pod_rate_gpu_memory_used_request
      %
      GPU Memory Utilization (per node)
      %
      GPU memory usage percentage in the node
      Pods
      k8s_pod_rate_gpu_memory_used_node
      %

      Creating and Editing an HPA by Using a YAML File

      You can create and edit an HPA by using a YAML file. The following example shows a configuration file that defines an HPA named "example". The HPA enables the system to trigger HPA for 1 or 2 Pods when the CPU usage reaches 1.
      Note
      TKE is compatible with the native resource types.
      apiVersion: autoscaling/v2beta1
      kind: HorizontalPodAutoscaler
      metadata:
        name: example
        namespace: default
        labels:
          qcloud-app: example
      spec:
        minReplicas: 1
        maxReplicas: 2
        metrics:
        - type: Pods# Support using Resource
          pods:
            metricName: k8s_pod_cpu_core_used
            targetAverageValue: "1"
        scaleTargetRef:
          apiVersion: apps/v1beta2
          kind: Deployment
          name: nginx
      
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