#placement-strategy

CPU Placement Strategy is a compute scheduling rule that chooses where workloads should run for general-purpose processor scheduling. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used CPU Placement Strategy when the service hit a compute ceiling, so the team could improve reliability and efficiency before the workload scaled up.”

Cache Placement Strategy is a compute scheduling rule that chooses where workloads should run for fast temporary data layer. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Cache Placement Strategy when the cache missed during peak traffic, so the team could improve reliability and efficiency before the workload scaled up.”

Cluster Placement Strategy is a compute scheduling rule that chooses where workloads should run for group of machines acting as one platform. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Cluster Placement Strategy when the cluster added a node pool, so the team could improve reliability and efficiency before the workload scaled up.”

Container Placement Strategy is a compute scheduling rule that chooses where workloads should run for packaged application runtime. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Container Placement Strategy when the image started on a new node, so the team could improve reliability and efficiency before the workload scaled up.”

Edge Placement Strategy is a compute scheduling rule that chooses where workloads should run for globally distributed runtime. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Edge Placement Strategy when the request arrived near a user, so the team could improve reliability and efficiency before the workload scaled up.”

GPU Placement Strategy is a compute scheduling rule that chooses where workloads should run for accelerated compute for parallel workloads. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used GPU Placement Strategy when the training job requested more memory, so the team could improve reliability and efficiency before the workload scaled up.”

Memory Placement Strategy is a compute scheduling rule that chooses where workloads should run for volatile runtime storage. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Memory Placement Strategy when the process approached its memory limit, so the team could improve reliability and efficiency before the workload scaled up.”

Queue Placement Strategy is a compute scheduling rule that chooses where workloads should run for asynchronous work buffer. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Queue Placement Strategy when the queue depth increased, so the team could improve reliability and efficiency before the workload scaled up.”

Scheduler Placement Strategy is a compute scheduling rule that chooses where workloads should run for placement of work onto resources. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Scheduler Placement Strategy when the cluster needed to place a job, so the team could improve reliability and efficiency before the workload scaled up.”

Serverless Placement Strategy is a compute scheduling rule that chooses where workloads should run for event-driven function execution. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Serverless Placement Strategy when the function received a traffic burst, so the team could improve reliability and efficiency before the workload scaled up.”

Storage Placement Strategy is a compute scheduling rule that chooses where workloads should run for persistent data and object access. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Storage Placement Strategy when the workload read a large dataset, so the team could improve reliability and efficiency before the workload scaled up.”

Virtual Machine Placement Strategy is a compute scheduling rule that chooses where workloads should run for isolated guest compute. It uses affinity, topology, availability, and cost signals so teams can improve reliability and efficiency while keeping evidence, reliability, and public-safe operational boundaries clear.

“The platform engineering team used Virtual Machine Placement Strategy when the VM migrated hosts, so the team could improve reliability and efficiency before the workload scaled up.”