Building Deterministic Schedulers

Most schedulers treat timing as a best-effort concern. In safety-critical domains, that isn't good enough. This post explores how constraint-propagation techniques from AI planning can produce schedulers with provable timing guarantees.

Key Insight

By modeling tasks as nodes in a temporal constraint network, we can enforce deadlines at the structural level—making missed deadlines impossible by construction.

The Approach

1. Define each task as a temporal variable with earliest/latest bounds
2. Propagate constraints using arc-consistency (AC-3)
3. Extract a feasible schedule via topological ordering

The result is a scheduler that cannot produce invalid schedules, eliminating an entire class of runtime failures.