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To enable HPA, you need to set the parameters as described below for each of the microservices individually.
The following screenshot illustrates the autoscaling parameters for webrunner microservice.
# Autoscaling[HPA] Values for Webrunner can be defined here You can find these parameters in the respective section of each microservice in the global values.yaml file.
| Parameter | Description | Sample value |
|---|---|---|
| autoscaling: |
...
| Parameter | Description | ||
|---|---|---|---|
| enabled | enabled: | Parameter to enable HPA by setting its value to true. | TRUE |
| minReplicas: | Minimum number of pods for a microservice. | 1 | |
| maxReplicas: | The maximum number of pods a microservice can scale up to. | 1 | |
| targetCPUUtilizationPercentage: | The percentage value of CPU utilization at which the autoscaler spins up a new pod. | targetmemoryUtilizationPercentage400 | |
| targetMemoryUtilizationPercentage: | The percentage value of memory utilization at which the autoscaler spins up a new pod. | 400 | |
| behavior: | |||
| scaleUp: | |||
| stabilizationWindowSeconds: | The duration (in seconds) for which the application keeps a watch on the spikes in the resource utilization by the currently running pods. This helps in determining whether scaling up is required or not. | 300 | |
| maxPodToScaleUp: | The maximum number of pods a microservice can scale up to at a time. | 2 | |
| periodSeconds: | The time duration (in seconds) that sets the frequency of tracking the spikes in the resource utilization by the currently running pods. | 60 | |
| scaleDown: | |||
| stabilizationWindowSeconds: | The duration (in seconds) for which the application keeps a watch for drop in resource utilization by the currently running pods. This helps in determining whether scaling down is required or not. | 300 | |
| maxPodToScaleDown: | The maximum number of pods a microservice can scale down to at a time. | 1 | |
| periodSeconds: | The time duration (in seconds) that sets the frequency of tracking the drop in the resource utilization by the currently running pods. | 60 |
Configuring HPA for runtime microservice
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The following screenshot illustrates the autoscaling parameters for runtime microservice. You can find these parameters in the runtimeImage: section in the global values.yaml file.
| Parameter | Description | Sample value |
|---|---|---|
| RUNTIME_AUTOSCALING_ENABLED: | Parameter to enable HPA by setting its value to true. | true |
| RUNTIME_MIN_POD: | Minimum number of pods. | 1 |
| RUNTIME_MAX_POD: | The maximum number of pods the runtime microservice can scale up to. | 1 |
| RUNTIME_AUTOSCALING_TARGETCPUUTILIZATIONPERCENTAGE: | The value of CPU utilization (in percentage) at which the autoscaler spins up a new pod. | 400 |
| RUNTIME_AUTOSCALING_TARGETMEMORYUTILIZATIONPERCENTAGE: | The value of memory utilization (in percentage) at which the autoscaler spins up a new pod. | 400 |
| RUNTIME_SCALE_UP_STABILIZATION_WINDOW_SECONDS: | The duration (in seconds) for which the application keeps a watch on the spikes in the resource utilization by the currently running pods. This helps in determining whether scaling up is required or not. | 300 |
| RUNTIME_MAX_POD_TO_SCALE_UP: | The maximum number of pods the runtime microservice can scale up to at a time. | 1 |
| RUNTIME_SCALE_UP_PERIOD_SECONDS: | The time duration (in seconds) that sets the frequency of tracking the spikes in the resource utilization by the currently running pods. | 60 |
| RUNTIME_SCALE_DOWN_STABILIZATION_WINDOW_SECONDS: | The duration (in seconds) for which the application keeps a watch for drop in resource utilization by the currently running pods. This helps in determining whether scaling down is required or not. | 300 |
| RUNTIME_MAX_POD_TO_SCALE_DOWN: | The maximum number of pods the runtime microservice can scale down to at a time. | 1 |
| RUNTIME_SCALE_DOWN_PERIOD_SECONDS: |
...
| The time duration (in seconds) that sets the frequency of tracking the drop in the resource utilization by the currently running pods. | 60 |