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An enterprise runs a batch and transaction processing workload on a Compute Engine regional Managed Instance Group (MIG). The workload faces diverse bottlenecks depending on customer behavior:
You need to configure an autoscaling policy that combines these signals to ensure the application maintains high performance without dropping requests.
How does the Compute Engine autoscaler evaluate these multiple signals to determine the target size of the MIG?
The autoscaler evaluates signals by strict priority hierarchy, where scaling schedules override Cloud Monitoring metrics, which in turn override CPU utilization.
The autoscaler calculates the recommended number of VM instances independently for each signal and provisions the largest resulting instance count.
The autoscaler sums together the recommended instance counts calculated for each individual signal up to the configured maximum number of replicas.
The autoscaler calculates the arithmetic average across all recommended instance counts from the configured signals to smooth out sudden scaling fluctuations.
The autoscaler evaluates signals by strict priority hierarchy, where scaling schedules override Cloud Monitoring metrics, which in turn override CPU utilization.
The autoscaler calculates the recommended number of VM instances independently for each signal and provisions the largest resulting instance count.
Compute Engine autoscaling allows a Managed Instance Group (MIG) to automatically adjust its number of virtual machine instances according to changing workload demands. When an autoscaling policy is configured with multiple signals—such as CPU utilization, Cloud Monitoring custom metrics, load balancing serving capacity, and scaling schedules—the autoscaler independently evaluates the required capacity for every single signal during each collection cycle.
In this scenario, the workload has multiple independent bottlenecks:
By taking the maximum (largest number of VMs) among all individual recommendations, the autoscaler guarantees that whichever bottleneck or schedule demands the most capacity is fully satisfied, preventing performance degradation or request drops.
StudyCerts
StudyCertsminNumReplicas and maxNumReplicas bounds across all combined evaluations.Picking the largest recommended instance count ensures high availability and eliminates under-provisioning. If one metric demands 20 VMs due to high CPU while a queue metric only requires 8 VMs, scaling to 20 VMs ensures the CPU-bound requests do not fail.
The autoscaler sums together the recommended instance counts calculated for each individual signal up to the configured maximum number of replicas.
The autoscaler calculates the arithmetic average across all recommended instance counts from the configured signals to smooth out sudden scaling fluctuations.