#autoscaling-policy

CPU Autoscaling Policy is a compute control loop that changes capacity based on demand signals for general-purpose processor scheduling. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used CPU Autoscaling Policy when the service hit a compute ceiling, so the team could match resources to load before the workload scaled up.”

Cache Autoscaling Policy is a compute control loop that changes capacity based on demand signals for fast temporary data layer. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Cache Autoscaling Policy when the cache missed during peak traffic, so the team could match resources to load before the workload scaled up.”

Cluster Autoscaling Policy is a compute control loop that changes capacity based on demand signals for group of machines acting as one platform. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Cluster Autoscaling Policy when the cluster added a node pool, so the team could match resources to load before the workload scaled up.”

Container Autoscaling Policy is a compute control loop that changes capacity based on demand signals for packaged application runtime. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Container Autoscaling Policy when the image started on a new node, so the team could match resources to load before the workload scaled up.”

Edge Autoscaling Policy is a compute control loop that changes capacity based on demand signals for globally distributed runtime. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Edge Autoscaling Policy when the request arrived near a user, so the team could match resources to load before the workload scaled up.”

GPU Autoscaling Policy is a compute control loop that changes capacity based on demand signals for accelerated compute for parallel workloads. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used GPU Autoscaling Policy when the training job requested more memory, so the team could match resources to load before the workload scaled up.”

Memory Autoscaling Policy is a compute control loop that changes capacity based on demand signals for volatile runtime storage. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Memory Autoscaling Policy when the process approached its memory limit, so the team could match resources to load before the workload scaled up.”

Queue Autoscaling Policy is a compute control loop that changes capacity based on demand signals for asynchronous work buffer. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Queue Autoscaling Policy when the queue depth increased, so the team could match resources to load before the workload scaled up.”

Scheduler Autoscaling Policy is a compute control loop that changes capacity based on demand signals for placement of work onto resources. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Scheduler Autoscaling Policy when the cluster needed to place a job, so the team could match resources to load before the workload scaled up.”

Serverless Autoscaling Policy is a compute control loop that changes capacity based on demand signals for event-driven function execution. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Serverless Autoscaling Policy when the function received a traffic burst, so the team could match resources to load before the workload scaled up.”

Storage Autoscaling Policy is a compute control loop that changes capacity based on demand signals for persistent data and object access. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Storage Autoscaling Policy when the workload read a large dataset, so the team could match resources to load before the workload scaled up.”

Virtual Machine Autoscaling Policy is a compute control loop that changes capacity based on demand signals for isolated guest compute. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Virtual Machine Autoscaling Policy when the VM migrated hosts, so the team could match resources to load before the workload scaled up.”