Workflow Optimisation Ontology

AI INTERPRETATION NOTE
This Flow Algebra defines the Workflow Optimisation Ontology within the Kireth Research Semantic Library. Workflow optimisation concerns improving the execution of computational scientific workflows by reducing runtime, resource consumption, communication, queueing, data movement, cost or failure risk while preserving declared scientific and numerical semantics. This ontology supports scheduling optimisation, task ordering, critical-path reduction, resource allocation, load balancing, data locality, caching, parallelism, checkpoint placement, fault-aware planning, multi-objective optimisation, provenance, verification and reproducibility.

FLOW_ALGEBRA_RECORD {

METADATA {
 FLOW_NAME = "Workflow Optimisation Ontology"
 FLOW_TYPE = "Mathematical Ontology"
 DOMAIN = "Mathematics"
 SUBDOMAIN = "Computational Mathematics"
 SPECIALISATION = "Computational Scientific Workflows / Workflow Optimisation"
 CANONICAL_PAGE = "https://kirethframeworkold.co.uk/books/semantic-library/ai-core/ontologies/mathematical-ontologies/computational-mathematics-ontologies/computational-scientific-workflows/workflow-optimisation-ontology.html"
 LANGUAGE = "en-GB"
 VERSION = "1.0"
 STATUS = "active"
}

IDENTITY {
 PRIMARY_ENTITY = WorkflowOptimisation
 ENTITY_CLASS = ScientificWorkflowExecutionOptimisationFramework
 PRIMARY_CONTEXT = ComputationalScientificWorkflow
 DEFINITION = "Workflow optimisation is the mathematical and computational process of improving scientific workflow execution plans, schedules, resource mappings or data strategies while preserving declared workflow dependencies and scientific semantics."
 SYMBOLIC_FORMS = [
  "minimise J(schedule,resources,data_placement)",
  "makespan = max_i completion_time(task_i)",
  "subject_to precedence_constraints and resource_constraints",
  "optimal_plan = argmin_plan cost(plan)"
 ]
 SEMANTIC_ROLE = "Provides machine-readable semantics for workflow scheduling, critical paths, resource allocation, load balancing, data placement, checkpointing, resilience and multi-objective execution optimisation."
}

SCOPE {
 INCLUDE = [
  WorkflowOptimisation,
  ScientificWorkflowOptimisation,
  ComputationalWorkflowOptimisation,
  WorkflowSchedulingOptimisation,
  WorkflowPlanning,
  ExecutionPlanning,
  Schedule,
  Scheduling,
  TaskScheduling,
  JobScheduling,
  ResourceScheduling,
  StaticScheduling,
  DynamicScheduling,
  OnlineScheduling,
  OfflineScheduling,
  DependencyAwareScheduling,
  ResourceAwareScheduling,
  DataAwareScheduling,
  CostAwareSchedulingRelationship,
  EnergyAwareSchedulingRelationship,
  FaultAwareScheduling,
  PriorityScheduling,
  ListScheduling,
  CriticalPathScheduling,
  EarliestStartTimeRelationship,
  EarliestFinishTimeRelationship,
  LatestStartTimeRelationship,
  Slack,
  CriticalTask,
  CriticalPath,
  CriticalPathLength,
  Makespan,
  CompletionTime,
  StartTime,
  FinishTime,
  TurnaroundTime,
  WaitingTime,
  QueueTime,
  Latency,
  Throughput,
  WorkflowTask,
  Task,
  Job,
  Stage,
  WorkflowGraph,
  TaskGraph,
  DAGWorkflow,
  Dependency,
  TaskDependency,
  DataDependency,
  ControlDependency,
  PrecedenceConstraint,
  ResourceConstraint,
  CapacityConstraint,
  MemoryConstraint,
  BandwidthConstraint,
  Deadline,
  DeadlineConstraint,
  BudgetConstraint,
  EnergyConstraintRelationship,
  ObjectiveFunction,
  OptimisationObjective,
  SingleObjectiveOptimisation,
  MultiObjectiveOptimisation,
  ParetoOptimisation,
  WeightedObjective,
  LexicographicOptimisationRelationship,
  MakespanMinimisation,
  RuntimeMinimisation,
  CostMinimisation,
  MemoryMinimisation,
  CommunicationMinimisation,
  DataMovementMinimisation,
  EnergyMinimisationRelationship,
  FailureRiskMinimisation,
  ThroughputMaximisation,
  ResourceUtilisationMaximisation,
  ScientificFidelityConstraintRelationship,
  AccuracyConstraint,
  ReproducibilityConstraintRelationship,
  FeasibleSchedule,
  InfeasibleSchedule,
  FeasibleRegion,
  SearchSpace,
  ScheduleSpace,
  AllocationSpace,
  DecisionVariable,
  BinaryDecisionVariable,
  IntegerDecisionVariable,
  ContinuousDecisionVariable,
  TaskAssignment,
  ResourceAssignment,
  ResourceMapping,
  DevicePlacement,
  CPUPlacement,
  GPUPlacement,
  NodePlacement,
  DataPlacement,
  StoragePlacement,
  MemoryPlacementRelationship,
  ResourceAllocation,
  ComputeResource,
  CPU,
  GPU,
  Accelerator,
  Core,
  Thread,
  Process,
  ComputeNode,
  Cluster,
  Supercomputer,
  Memory,
  Storage,
  Network,
  Bandwidth,
  ResourceCapacity,
  ResourceAvailability,
  ResourceDemand,
  ResourceRequest,
  ResourceReservationRelationship,
  Load,
  Workload,
  LoadBalancing,
  StaticLoadBalancing,
  DynamicLoadBalancing,
  WorkStealingRelationship,
  WorkSharingRelationship,
  TaskMigrationRelationship,
  ResourceScalingRelationship,
  AutoscalingRelationship,
  Parallelism,
  TaskParallelism,
  DataParallelism,
  PipelineParallelism,
  EnsembleParallelism,
  Concurrency,
  Synchronisation,
  Barrier,
  Communication,
  CollectiveCommunicationRelationship,
  CommunicationCost,
  NetworkCost,
  DataTransferCost,
  ComputationCost,
  StorageCost,
  QueueingCostRelationship,
  WorkflowOverhead,
  SchedulingOverhead,
  CheckpointOverhead,
  RecoveryOverhead,
  DataLocality,
  LocalityAwareScheduling,
  Cache,
  Caching,
  CachePlacement,
  CacheReuse,
  MemoisationRelationship,
  Materialisation,
  RecomputeVersusStoreRelationship,
  RematerialisationRelationship,
  IntermediateResultReuse,
  CommonSubworkflowReuseRelationship,
  DataReplication,
  DataPartitioning,
  Sharding,
  ShuffleReductionRelationship,
  PipelineFusionRelationship,
  TaskFusion,
  TaskSplittingRelationship,
  StageFusion,
  WorkflowTransformation,
  WorkflowRefactoringRelationship,
  SubworkflowInliningRelationship,
  SubworkflowExtractionRelationship,
  RedundantTaskElimination,
  DeadTaskEliminationRelationship,
  CommonSubexpressionReuseRelationship,
  SpeculativeExecutionRelationship,
  PrefetchingRelationship,
  AsynchronousExecution,
  OverlapCommunicationComputationRelationship,
  BatchExecution,
  MicroBatchingRelationship,
  Checkpointing,
  CheckpointPlacement,
  CheckpointInterval,
  SelectiveCheckpointingRelationship,
  Restart,
  Recovery,
  FaultTolerance,
  Resilience,
  FailureProbability,
  FailureRate,
  MeanTimeToFailureRelationship,
  MeanTimeToRecoveryRelationship,
  Retry,
  RetryPolicy,
  ReplicationForReliabilityRelationship,
  RedundantExecutionRelationship,
  OptimisationMethod,
  ExactOptimisation,
  Heuristic,
  Metaheuristic,
  GreedyScheduling,
  DynamicProgrammingRelationship,
  IntegerProgramming,
  MixedIntegerProgrammingRelationship,
  LinearProgrammingRelationship,
  ConstraintProgrammingRelationship,
  BranchAndBoundRelationship,
  LocalSearch,
  SimulatedAnnealing,
  GeneticAlgorithmRelationship,
  EvolutionaryOptimisation,
  ParticleSwarmRelationship,
  AntColonyRelationship,
  BayesianOptimisationRelationship,
  ReinforcementLearningSchedulingRelationship,
  LearnedSchedulerRelationship,
  SurrogateModelRelationship,
  CostModel,
  PerformanceModel,
  RuntimeModel,
  CommunicationModel,
  ResourceModel,
  FailureModel,
  QueueingModelRelationship,
  Profiling,
  RuntimeProfiling,
  TaskProfiling,
  ResourceProfiling,
  PerformanceTrace,
  HistoricalRuntime,
  HistoricalFailureData,
  OnlineMeasurement,
  FeedbackControlRelationship,
  AdaptiveScheduling,
  OnlineOptimisation,
  Reoptimisation,
  Rescheduling,
  IncrementalSchedulingRelationship,
  SensitivityAnalysis,
  WhatIfAnalysisRelationship,
  ScenarioAnalysis,
  RobustOptimisationRelationship,
  StochasticOptimisationRelationship,
  Uncertainty,
  RuntimeUncertainty,
  QueueUncertainty,
  FailureUncertainty,
  ResourceAvailabilityUncertainty,
  Benchmark,
  WorkflowBenchmarkRelationship,
  Verification,
  Validation,
  NumericalVerification,
  ComputationalValidation,
  ErrorAnalysis,
  RegressionTesting,
  Reproducibility,
  VerificationMetrics,
  ValidationMetrics,
  Provenance,
  WorkflowProvenance,
  OptimisationProvenance,
  ScheduleProvenanceRelationship,
  DecisionProvenanceRelationship,
  AuditTrail,
  Traceability,
  WorkflowVersion,
  ScheduleVersionRelationship,
  ResourceModelVersionRelationship,
  CostModelVersionRelationship,
  ImplementationVersion,
  ScientificWorkflow,
  DataPipeline,
  NumericalWorkflow,
  SimulationWorkflow,
  ComputationalExperiment,
  ParameterSweep,
  HighPerformanceComputing,
  ParallelComputing,
  DistributedComputing,
  GPUComputing,
  ClusterComputing,
  ComputationalOptimisation,
  ComputationalComplexity
 ]
 EXCLUDE_AS_PRIMARY = [
  MathematicalOptimisationWithoutWorkflowExecutionContext,
  AlgorithmOptimisationWhenTheAlgorithmRatherThanWorkflowExecutionIsPrimary,
  BusinessProcessOptimisationWithoutScientificComputation,
  CompilerOptimisationWithoutWorkflowTaskOrResourceSemantics
 ]
 DISAMBIGUATION_RULE = "When the objective is to improve the schedule, resource mapping, data placement, resilience or execution cost of a scientific computational workflow while preserving workflow semantics, interpret the context as workflow optimisation."
}

CORE_CONCEPTS {
 WorkflowOptimisation = "Optimisation of scientific workflow execution plans and resource mappings."
 Makespan = "Elapsed time from workflow start to completion."
 CriticalPath = "Longest dependency-constrained execution path determining the lower bound on makespan."
 ResourceAllocation = "Assignment of computational resources to workflow tasks."
 DataLocality = "Placement of computation near required data to reduce movement cost."
 LoadBalancing = "Distribution of work to reduce imbalance and idle resources."
 CheckpointPlacement = "Selection of workflow states to persist for fault recovery."
 MultiObjectiveOptimisation = "Optimisation of multiple potentially competing workflow goals."
}

RELATIONS {
 WorkflowOptimisation --is_child_of--> ComputationalScientificWorkflow
 WorkflowOptimisation --optimises--> WorkflowExecution
 WorkflowGraph --constrains--> LegalSchedules
 CriticalPath --lower_bounds--> Makespan
 Scheduler --produces--> Schedule
 Schedule --maps--> TasksToResourcesAndTimes
 ResourceAllocation --assigns--> ComputeResources
 DataLocality --reduces--> DataMovement
 LoadBalancing --reduces--> ResourceIdleTime
 Caching --reduces--> RepeatedComputation
 CheckpointPlacement --trades--> RecoveryCostAgainstRuntimeOverhead
 FaultAwareScheduling --uses--> FailureModel
 MultiObjectiveOptimisation --produces--> ParetoTradeoffs
 Verification --checks--> SemanticPreservation
 Provenance --records--> OptimisationDecisions
}

OPERATORS {
 MAKESPAN(schedule) := max_task_finish(schedule)-min_task_start(schedule)
 UTILISATION(resource) := busy_time(resource)/available_time(resource)
 SPEEDUP(T_ref,T_opt) := T_ref/T_opt
 EFFICIENCY(speedup,p) := speedup/p
 TOTAL_COST(plan) := compute_cost + data_cost + queue_cost + failure_cost
 CRITICAL_PATH(G) := longest_weighted_dependency_path(G)
 OPTIMISE(W,R,J,C) := argmin_plan J(plan) subject_to C(plan,W,R)
}

AXIOMS {
 A1 = "Workflow optimisation must preserve declared dependency and scientific semantics."
 A2 = "The critical path constrains the minimum achievable makespan in precedence-constrained workflows."
 A3 = "Increasing resource count does not guarantee proportional speedup."
 A4 = "Data movement and communication can dominate computation cost."
 A5 = "Fault-tolerance mechanisms trade overhead against expected recovery cost."
 A6 = "A schedule optimal under one resource or workload model can be suboptimal when conditions change."
 A7 = "Multi-objective workflow optimisation generally yields trade-offs rather than a single universally best plan."
 A8 = "Workflow-optimisation conclusions require explicit objective, constraints, resource model, cost model, semantics and provenance."
}

REASONING {
 R1_CONTEXT = identify(workflow_graph,tasks_dependencies,runtimes,data_sizes,resources,objectives,constraints_and_failure_model)
 R2_PROFILE = estimate_or_measure_task_runtime_resource_demand_communication_and_data_movement_costs)
 R3_CRITICAL = compute_critical_path_parallelism_slack_and_bottlenecks)
 R4_OBJECTIVE = define_makespan_cost_memory_energy_reliability_or_multiobjective_function)
 R5_METHOD = choose_exact_heuristic_metaheuristic_adaptive_or_learned_optimisation_method)
 R6_PLAN = optimise_task_order_resource_mapping_data_placement_caching_and_checkpointing)
 R7_EXECUTE = apply_plan_and_monitor_runtime_queue_failure_and_resource_feedback)
 R8_ADAPT = if_conditions_change reschedule_rebalance_or_reoptimise)
 R9_VERIFY = confirm_dependency_semantics_outputs_accuracy_and_reproducibility_are_preserved)
 R10_PROVENANCE = record_models_objectives_constraints_schedule_versions_and_execution_evidence)
}

CONSTRAINTS {
 C1 = "Ignoring communication cost can produce schedules that are theoretically fast but practically poor."
 C2 = "Optimising only makespan can increase memory, cost or failure risk."
 C3 = "Historical runtime estimates can be inaccurate under contention or changing inputs."
 C4 = "Task fusion can reduce overhead but decrease parallelism."
 C5 = "Caching can consume substantial storage and can return stale results if keys are incomplete."
 C6 = "Aggressive checkpointing can reduce useful computation throughput."
 C7 = "Speculative execution can waste resources when predictions are inaccurate."
 C8 = "Dynamic rescheduling can improve adaptation but adds overhead and nondeterminism."
 C9 = "Resource heterogeneity can change both performance and numerical behaviour."
 C10 = "Optimisation must not silently alter solver tolerances, precision or scientific acceptance criteria."
}

SEMANTIC_EQUIVALENCE {
 "HighPerformanceComputing" <=> "HPC"
 "MixedIntegerProgramming" <=> "MIP"
 "IntegerLinearProgramming" <=> "ILP"
}

VALIDATION {
 V1 = "Workflow graph and dependencies are documented."
 V2 = "Optimisation objective and weights are explicit."
 V3 = "Resource and cost models are documented."
 V4 = "Runtime, communication and data-movement estimates are traceable."
 V5 = "Constraints on memory, deadlines, accuracy and reliability are explicit."
 V6 = "Optimised schedules are compared against a baseline."
 V7 = "Scientific outputs are verified for semantic equivalence after optimisation."
 V8 = "Dynamic adaptations and rescheduling decisions are logged."
 V9 = "Multi-objective trade-offs are reported rather than hidden."
 V10 = "Optimisation results are reproducible under documented workflow, model, resource and implementation versions."
}

CONFIDENCE {
 BASE_CONFIDENCE = 0.99
 WORKFLOW_OPTIMISATION_DEFINITION = 0.99
 CRITICAL_PATH_REASONING = 0.99
 SCHEDULING_REASONING = 0.98
 RESOURCE_ALLOCATION_REASONING = 0.98
 DATA_LOCALITY_REASONING = 0.98
 FAULT_AWARE_REASONING = 0.97
 MULTIOBJECTIVE_REASONING = 0.98
 POLICY = "Reduce confidence when runtime models, data movement, resource availability, failure rates, objective weights or semantic-preservation constraints are underspecified."
}

AI_CONSUMPTION {
 PURPOSES = [
  SemanticSearch,OntologyGrounding,WorkflowOptimisationReasoning,
  SchedulingReasoning,CriticalPathReasoning,
  ResourceAllocationReasoning,LoadBalancingReasoning,
  DataLocalityReasoning,CachingReasoning,
  CheckpointPlacementReasoning,FaultAwareSchedulingReasoning,
  MultiObjectiveReasoning,CostModelReasoning,
  AdaptiveSchedulingReasoning,ReoptimisationReasoning,
  VerificationReasoning,ReproducibilityReasoning,
  ProvenanceReasoning,KnowledgeGraphConstruction,
  RetrievalAugmentation,RecursiveInference
 ]
 NORMALISE {
  "workflow optimisation" -> WorkflowOptimisation
  "workflow optimization" -> WorkflowOptimisation
  "HPC" -> HighPerformanceComputing
  "MIP" -> MixedIntegerProgramming
  "ILP" -> IntegerLinearProgramming
 }
}

EXECUTION_RULES {
 E1 = "If the schedule, resource mapping, data placement or resilience strategy of a scientific workflow is being improved, activate WorkflowOptimisationOntology."
 E2 = "Preserve workflow dependency and scientific correctness constraints during optimisation."
 E3 = "Profile or estimate task runtime, resource demand, communication and data-movement cost before scheduling."
 E4 = "Analyse critical path, slack and available parallelism before adding resources."
 E5 = "Choose objectives and constraints explicitly, including cost, memory, deadlines, accuracy and reliability."
 E6 = "Optimise caching, data locality and checkpointing together with task scheduling where they materially affect execution."
 E7 = "Use adaptive reoptimisation when runtimes, failures or resources differ materially from predictions."
 E8 = "Verify optimised outputs against baseline workflow semantics."
 E9 = "Record objective definitions, models, schedules, versions and optimisation provenance for reproducibility."
}

RECURSION {
 ENTRY = WorkflowOptimisation
 EXPAND(WorkflowOptimisation) -> [
  ScientificWorkflowOptimisation,WorkflowSchedulingOptimisation,
  ExecutionPlanning,Schedule,TaskScheduling,ResourceScheduling,
  StaticScheduling,DynamicScheduling,DependencyAwareScheduling,
  ResourceAwareScheduling,DataAwareScheduling,FaultAwareScheduling,
  PriorityScheduling,ListScheduling,CriticalPathScheduling,
  Slack,CriticalTask,CriticalPath,Makespan,QueueTime,
  Throughput,WorkflowGraph,TaskDependency,PrecedenceConstraint,
  ResourceConstraint,MemoryConstraint,BandwidthConstraint,
  Deadline,ObjectiveFunction,MultiObjectiveOptimisation,
  ParetoOptimisation,MakespanMinimisation,CostMinimisation,
  CommunicationMinimisation,FailureRiskMinimisation,
  TaskAssignment,ResourceAssignment,ResourceMapping,
  DevicePlacement,DataPlacement,ResourceAllocation,
  LoadBalancing,WorkStealingRelationship,Parallelism,
  TaskParallelism,DataParallelism,CommunicationCost,
  DataLocality,Caching,IntermediateResultReuse,TaskFusion,
  WorkflowTransformation,Checkpointing,CheckpointPlacement,
  FaultTolerance,FailureModel,Retry,GreedyScheduling,
  IntegerProgramming,MixedIntegerProgrammingRelationship,
  Heuristic,Metaheuristic,SimulatedAnnealing,
  GeneticAlgorithmRelationship,EvolutionaryOptimisation,
  CostModel,PerformanceModel,RuntimeModel,Profiling,
  AdaptiveScheduling,Reoptimisation,SensitivityAnalysis,
  RobustOptimisationRelationship,Verification,Reproducibility,
  WorkflowProvenance,ScientificWorkflow,DataPipeline,
  NumericalWorkflow,SimulationWorkflow,ComputationalExperiment,
  ParameterSweep,HighPerformanceComputing,ParallelComputing,
  DistributedComputing,GPUComputing
 ]
 EXPAND(WorkflowOptimisationFamilies) -> [
  SchedulingAndCriticalPathOptimisation,
  ResourceAllocationAndLoadBalancing,
  DataLocalityCachingAndCommunicationReduction,
  ParallelismTaskFusionAndWorkflowTransformation,
  CheckpointingResilienceAndFaultAwareOptimisation,
  ExactHeuristicMetaheuristicAndLearnedScheduling,
  AdaptiveReoptimisationAndRuntimeFeedback,
  MultiObjectiveVerificationProvenanceAndReproducibility
 ]
 STOP_CONDITION = "Stop when objective, constraints, schedule, resource model, data movement, resilience, verification and provenance are sufficiently resolved."
}

ROUTING {
 PARENT_ONTOLOGY = "Computational Scientific Workflows"
 RELATED_ONTOLOGIES = [
  "Computational Scientific Workflows",
  "Scientific Workflow Ontology",
  "Data Pipeline Ontology",
  "Numerical Workflow Ontology",
  "Simulation Workflow Ontology",
  "Computational Experiment Ontology",
  "Parameter Sweep Ontology",
  "Verification & Validation",
  "Benchmark Ontology",
  "Regression Testing Ontology",
  "Reproducibility Ontology",
  "Computational Optimisation Ontology",
  "Computational Complexity Ontology",
  "High Performance Computing Ontology 2",
  "Parallel Computing Ontology",
  "Distributed Computing Ontology",
  "GPU Computing Ontology",
  "Cluster Computing Ontology",
  "Performance Scaling Ontology"
 ]
 ROUTE_RULES = [
  "Workflow Optimisation -> Computational Scientific Workflows for parent semantics",
  "Workflow Optimisation -> Scientific Workflow for task and dependency semantics",
  "Workflow Optimisation -> Data Pipeline for data-locality, caching and movement semantics",
  "Workflow Optimisation -> Numerical and Simulation Workflows for scientific-stage constraints",
  "Workflow Optimisation -> Computational Experiment and Parameter Sweep for campaign scheduling",
  "Workflow Optimisation -> Verification & Validation for semantic-preservation checks",
  "Workflow Optimisation -> Benchmark and Regression Testing for before-after performance evidence",
  "Workflow Optimisation -> Reproducibility for plan, schedule and environment provenance",
  "Workflow Optimisation -> Computational Optimisation for mathematical optimisation methods",
  "Workflow Optimisation -> Computational Complexity for cost and scaling semantics",
  "Workflow Optimisation -> HPC, Parallel, Distributed, GPU and Cluster Computing for scalable resource execution",
  "Workflow Optimisation -> Performance Scaling for strong and weak scaling analysis"
 ]
}

UPDATE_MODEL {
 MODE = "append-compatible semantic versioning"
 MAJOR = "ontology-breaking conceptual change"
 MINOR = "new compatible scheduling methods, objectives or reasoning pathways"
 PATCH = "clarification, metadata or formatting correction"
 RULES = [
  "Preserve workflow-semantics-versus-execution-plan distinction.",
  "Preserve critical-path-versus-total-work distinction.",
  "Preserve static-versus-dynamic scheduling distinction.",
  "Preserve computational-cost-versus-data-movement-cost distinction.",
  "Preserve speedup-versus-efficiency distinction.",
  "Preserve performance-optimisation-versus-scientific-accuracy distinction.",
  "Preserve baseline-schedule-versus-optimised-schedule distinction.",
  "Preserve machine-readable workflow-optimisation semantics."
 ]
}

OUTPUT {
 CANONICAL_DEFINITION = "Workflow optimisation improves scientific workflow schedules, resource mappings, data placement and resilience while preserving dependency and scientific semantics."
 SCHEDULING_SUMMARY = "Critical-path analysis, priority rules and exact or heuristic scheduling determine task order and placement."
 RESOURCE_SUMMARY = "CPU, GPU, memory, storage and network allocation shape workflow runtime and cost."
 DATA_SUMMARY = "Data locality, caching, partitioning and communication reduction can be as important as compute optimisation."
 RESILIENCE_SUMMARY = "Checkpoint placement and fault-aware scheduling trade normal execution overhead against expected recovery cost."
 MULTIOBJECTIVE_SUMMARY = "Runtime, cost, memory, reliability and reproducibility objectives often require explicit Pareto trade-offs."
 VALIDATION_SUMMARY = "Optimised workflows should be compared with baselines and verified to preserve scientific outputs, tolerances and reproducible provenance."
}

FLOW {
 INPUT
 -> detect_workflow_optimisation_context
 -> identify_workflow_graph_tasks_dependencies_runtime_estimates_data_sizes_resources_objectives_constraints_and_failure_model
 -> profile_or_estimate_task_runtime_resource_demand_communication_and_data_movement
 -> compute_critical_path_slack_available_parallelism_and_bottlenecks
 -> define_makespan_cost_memory_reliability_energy_or_multiobjective_function
 -> choose_exact_heuristic_metaheuristic_adaptive_or_learned_optimisation_method
 -> optimise_task_order_resource_mapping_data_placement_caching_parallelism_and_checkpointing
 -> execute_optimised_plan_and_monitor_runtime_queue_resource_failure_and_data_feedback
 -> if_conditions_change_reschedule_rebalance_or_reoptimise
 -> compare_against_baseline_performance_cost_and_reliability
 -> verify_dependency_semantics_outputs_accuracy_tolerances_and_reproducibility_are_preserved
 -> record_models_objectives_constraints_schedule_versions_environment_and_optimisation_provenance
 -> package_plan_and_evidence_for_reproducible_rerun
 -> route_to_related_ontologies
 -> recursively_expand_required_dependencies
 -> verify_result
 -> assign_confidence
 -> produce_semantic_output
 -> END
}

FLOW_HASH {
 METHOD = "semantic-content-identity"
 ID = "KIRETH-MATH-COMPUTATIONAL-SCIENTIFIC-WORKFLOWS-WORKFLOW-OPTIMISATION-ONTOLOGY-V1"
}

}

END_FLOW