Schedulern

Schedulern is a conceptual scheduling framework designed to manage task execution in modern multi-core and distributed computing environments. It provides a policy-driven interface for selecting the next task to run, abstracting the details of the underlying execution engine. The framework is intended for experimentation, research, and education, allowing researchers to compare scheduling strategies under a common model.

The architecture of Schedulern centers on modularity and extensibility. It includes a core scheduling loop that receives task events, a policy module that defines the decision criteria, a resource model (covering CPU cores, memory bandwidth, and energy considerations), and a telemetry component that records performance metrics. A pluggable policy API enables researchers to implement, mix, and compare different strategies without modifying the core runtime. Schedulern supports both centralized and distributed scheduling scenarios and can simulate preemption, context switching, and workload heterogeneity.

Scheduling policies commonly explored with Schedulern include fixed-priority and round-robin approaches, earliest-deadline-first, lottery scheduling, fair-share scheduling, aging to prevent starvation, and energy-aware policies that trade performance for power efficiency. The framework emphasizes the ability to combine objectives through configurable utility functions or policy composition, enabling multi-objective optimization.

Applications and status: Schedulern serves as a teaching tool and a research instrument for evaluating scheduling concepts before integration into production systems. While described in academic literature and available in reference implementations within simulation environments and teaching runtimes, it is not a standardized product and is typically used to prototype and study scheduling ideas.