Horizontal Pod Autoscaling (HPA) governs the spinning up of additional pods when the existing resources (CPU and Memory) of the microservice are exhausted. The deletion of the additional pods occurs as and when the resources are free or restored for the microservice. In Adeptia Connect, Autoscaling is by default enabled. You can enable HPA in Adeptia Connect by setting the required parameters in the global values.yaml file.
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| Parameter | Description | Default value | ||||||
|---|---|---|---|---|---|---|---|---|
autoscaling:
| ||||||||
enabled: | Parameter to enable HPA by setting its value to true. | true | ||||||
| type: | Parameter to define whether you want the autoscaling to happen based on cpu or memory or both. The possible values for this parameter can be cpu, memory, and cpu-memory. | cpu | ||||||
| minReplicas: | Minimum number of pods for a microservice. | 1 | ||||||
| maxReplicas: | The maximum number of pods a microservice can scale up to. | 1 | ||||||
| targetCPUUtilizationPercentage: | Value in percentage of CPU requests set in the global values.yaml for the pods at which the HPA spins up a new pod. | 400 | ||||||
| targetMemoryUtilizationPercentage: | Value in percentage of memory requests set in the global values.yaml for the podsat which the HPA 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. | 1 | ||||||
| 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 |
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| Parameter | Description | Default 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_TYPE | Parameter to define whether you want the autoscaling to happen based on cpu or memory or both. The possible values for this parameter can be cpu, memory, and cpu-memory. | cpu | ||||||
| RUNTIME_AUTOSCALING_TARGETCPUUTILIZATIONPERCENTAGE: | Value in percentage of CPU requests set in the global values.yaml for the runtime pods at which the HPA spins up a new pod. | 400 | ||||||
| RUNTIME_AUTOSCALING_TARGETMEMORYUTILIZATIONPERCENTAGE: | Value in percentage of memory requests set in the global values.yaml for the runtime podsat which the HPA 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 |
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