schedulerdriven

Schedulerdriven is a software architectural pattern in which a central scheduler governs the execution of tasks by deciding when to run them, where to run them, and in what order. In a schedulerdriven system, task definitions describe units of work and their inputs and outputs, while the scheduler encodes dependencies, priorities, and resource constraints and assigns work to workers or execution nodes. The approach emphasizes decoupling between task specification and execution, enabling dynamic adaptation to workload, latency requirements, and failures.

Core concepts include task graphs or dependency structures, priority policies, resource awareness (CPU, memory, I/O), time-based triggers, preemption, backpressure, and fault tolerance. Scheduling policies may be push-based, where the scheduler actively distributes tasks, or pull-based or hybrid, where workers request tasks. Architectures can range from centralized schedulers controlling many workers to distributed schedulers with hierarchical or federated coordination.

Common domains for schedulerdriven systems include batch processing pipelines, data analytics workflows, cloud orchestration and platform runtimes, and high-performance computing clusters. The pattern supports a variety of implementations, from centralized schedulers to distributed or federated models, often integrating with monitoring and failure-recovery mechanisms.

Benefits of the schedulerdriven approach include improved resource utilization, predictable throughput, policy enforcement, and easier recovery through re-scheduling. Challenges involve scheduling overhead, system complexity, potential for contention or starvation, and nondeterminism in distributed environments.

Examples of scheduler-driven systems include Kubernetes for pod scheduling, Apache Airflow for DAG-based workflow orchestration, Slurm for cluster job scheduling, and Celery-based task pipelines. The term is used in discussions of orchestration patterns and workflow management.