SpanCriticalPathScenario
Agents scenarios · Implementation: mlsysim/engine/scenario_definitions/agents.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
One trial’s observed spans on a shared clock, the spans each waits for, and one intervention.
Notes
Equation: result evaluates the configured solver; its contract owns the governing equations.
Assumptions: Observed spans share a clock and dependencies describe the modeled intervention schedule.
Excludes: Causal discovery, contention induced by rescheduling, and missing spans.
References
Derivation: The scenario binds declared inputs to SpanCriticalPathModel. Those implementations document their derivations and sources.
Examples
Execute the registered worked inputs; this is a modeled calculation.
>>> from mlsysim import Scenarios
>>> case = Scenarios.RepairTrialSpanTimeline
>>> value = case.result
>>> print(f"{value.critical_path:.3f~P}")
11.200 sAttributes
| Name | Description |
|---|---|
| depends_on | |
| intervals | |
| intervention | |
| result | Evaluate the configured inputs with mlsysim.solvers.SpanCriticalPathModel.solve. |
Methods
| Name | Description |
|---|---|
| duration | Duration of the named observed span in seconds, end minus start. |
duration
engine.scenario_types.SpanCriticalPathScenario.duration(span)Duration of the named observed span in seconds, end minus start.
Equation: start, end = self.intervals[span] return (end - start).to(‘s’)
intervals
Type: Dict[str, Any].
Declared default: required.
depends_on
Type: Dict[str, List[str]].
Declared default: required.
intervention
Type: Dict[str, Any].
Declared default: {}.
result
engine.scenario_types.SpanCriticalPathScenario.result
Evaluate the configured inputs with mlsysim.solvers.SpanCriticalPathModel.solve.
The returned typed result retains the solver’s documented units, assumptions, and feasibility semantics; this property adds no measurement or safety claim. Equation: result = SpanCriticalPathModel().solve(...) using the fields shown below.