#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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.