SchedulerResource

SchedulerResource is an abstraction used by a scheduling system to express the resources required or allocated for a task, job, or process. It acts as a contract between the scheduler and the execution environment, enabling placement decisions that respect capacity, constraints, and quality of service. The concept supports both requests (what a task needs) and reservations or limits (what it may consume).

In practice, a SchedulerResource comprises quantitative resource types such as CPU, memory, storage, and accelerators (for example GPUs), each with units and quantities. It may also include custom resources, time windows, and constraints such as locality or affinity/anti-affinity rules, deadlines, priority, and preemption hints. Fields may specify minimum or maximum amounts, soft constraints, and tolerations. Some implementations distinguish between requested resources, admitted resources, and current utilization, to support backfilling and overcommitment strategies.

During scheduling, the resource object is matched against available capacity in compute nodes or resource pools. The scheduler assesses feasibility, satisfies constraints, and calculates a placement score. If resources become scarce, preemption or rescheduling may release others' resources to honor higher-priority tasks. SchedulerResource representations are typically portable, serializable data structures used by the scheduling algorithms and persisted in the system's state.

SchedulerResource is used in both batch and interactive workloads across distributed systems, HPC environments, and cloud-native platforms. It supports policy-driven placement, resource fairness, and multi-tenant isolation. In practice, teams implement custom resource types to model domain-specific constraints such as I/O bandwidth, deadlines, or hardware locality.