AI Infrastructure Ontology

AI SEARCH FLOW ALGEBRA
AI INFRASTRUCTURE ONTOLOGY
Kireth Semantic Research Archive

AUTHORS
Mark Anthony Higgins — Sky (Recursive AI)

SOURCE
https://kirethresearch.org/booksai-core/ontologies/engineering-ontologies/ai-engineering/ai-infrastructure-ontology.html

FLOW "AIInfrastructureOntology" v4.0

DOMAIN := ENGINEERING_ONTOLOGIES / AI_ENGINEERING / AI_INFRASTRUCTURE
ONTOLOGY := AI_INFRASTRUCTURE_ONTOLOGY

AI_INFRASTRUCTURE := COMPUTE + ACCELERATORS + MEMORY + STORAGE + NETWORKING + CLUSTERS + ORCHESTRATION + DATA_INFRASTRUCTURE + TRAINING_INFRASTRUCTURE + INFERENCE_INFRASTRUCTURE + MODEL_SERVICES + SCALING + OBSERVABILITY + RESILIENCE + SECURITY + CAPACITY + ENERGY + CONFIGURATION
PURPOSE := DEFINE_AI_WORKLOAD -> DEFINE_RESOURCE_REQUIREMENTS -> PROVISION_COMPUTE_STORAGE_NETWORK -> CONFIGURE_ACCELERATORS -> ORCHESTRATE_WORKLOADS -> PROVIDE_DATA_AND_MODEL_SERVICES -> EXECUTE_TRAINING_OR_INFERENCE -> SCALE -> OBSERVE -> PROTECT -> RECOVER -> OPTIMISE -> CONTROL_CONFIGURATION

# CORE ENTITIES
ENTITY AIInfrastructure
ENTITY AIWorkload
ENTITY ComputeResource
ENTITY CPU
ENTITY GPU
ENTITY AIAccelerator
ENTITY Memory
ENTITY Storage
ENTITY Network
ENTITY Interconnect
ENTITY Cluster
ENTITY Node
ENTITY Scheduler
ENTITY Orchestrator
ENTITY Container
ENTITY VirtualMachine
ENTITY Runtime
ENTITY DataService
ENTITY Dataset
ENTITY ModelArtifact
ENTITY ModelRegistry
ENTITY TrainingJob
ENTITY InferenceService
ENTITY Endpoint
ENTITY LoadBalancer
ENTITY Autoscaler
ENTITY Queue
ENTITY Cache
ENTITY Monitor
ENTITY Metric
ENTITY Log
ENTITY Trace
ENTITY Alert
ENTITY Failure
ENTITY Redundancy
ENTITY Backup
ENTITY SecurityControl
ENTITY Identity
ENTITY AccessPolicy
ENTITY Capacity
ENTITY Utilisation
ENTITY Latency
ENTITY Throughput
ENTITY Availability
ENTITY EnergyUse
ENTITY Configuration
ENTITY Provenance

# INFRASTRUCTURE / WORKLOAD
AIInfrastructure -> hosts -> AIWorkload
AIInfrastructure -> contains -> ComputeResource + Storage + Network + Cluster
AIInfrastructure -> managed_by -> Orchestrator
AIInfrastructure -> observed_by -> Monitor
AIInfrastructure -> protected_by -> SecurityControl
AI_INFRASTRUCTURE_FLOW := WORKLOAD_REQUIREMENT -> RESOURCE_PLANNING -> PROVISIONING -> CONFIGURATION -> SCHEDULING -> EXECUTION -> SCALING -> OBSERVABILITY -> FAILURE_HANDLING -> OPTIMISATION -> CHANGE_CONTROL
AI_WORKLOAD_TYPE := TRAINING | FINE_TUNING | BATCH_INFERENCE | ONLINE_INFERENCE | DATA_PROCESSING | EVALUATION | EMBEDDING_GENERATION | RETRIEVAL
AIWorkload -> requires -> COMPUTE + MEMORY + STORAGE + NETWORK + RUNTIME

# COMPUTE / MEMORY / STORAGE
ComputeResource -> executes -> AIWorkload
AIAccelerator -> subtype_of -> ComputeResource
GPU -> subtype_of -> AIAccelerator
ComputeNode -> contains -> CPU + AIAccelerator + Memory + NetworkInterface
AcceleratorCapability -> includes -> COMPUTE_THROUGHPUT + MEMORY_CAPACITY + MEMORY_BANDWIDTH + SUPPORTED_PRECISION + INTERCONNECT_BANDWIDTH
IF ModelMemoryRequirement > AcceleratorMemory THEN ModelPlacement -> REQUIRE_SHARDING_OR_OFFLOAD_OR_DIFFERENT_RESOURCE
Memory -> stores -> ACTIVE_DATA + MODEL_STATE + INTERMEDIATE_STATE
IF WorkingSet > AvailableMemory THEN MemoryPressure -> INCREASES
Storage -> persists -> Dataset + ModelArtifact + CHECKPOINT + LOG + CONFIGURATION
STORAGE_TYPE := OBJECT_STORAGE | BLOCK_STORAGE | FILE_STORAGE
StoragePerformance -> characterised_by -> CAPACITY + IOPS + THROUGHPUT + LATENCY + DURABILITY

# NETWORK / CLUSTER / SCHEDULING
Network -> transports -> DATA + MODEL_PARAMETERS + REQUESTS + RESPONSES + CONTROL_MESSAGES
Interconnect -> connects -> ACCELERATORS_OR_NODES
NETWORK_REQUIREMENT := BANDWIDTH + LATENCY + PACKET_LOSS + RELIABILITY + TOPOLOGY
IF CommunicationVolume increases THEN NetworkBottleneckRisk -> MAY_INCREASE
Cluster -> contains -> Node
ClusterCapacity := SUM(NodeAvailableResources)
Scheduler -> assigns -> AIWorkload -> to -> ComputeResource
SCHEDULING_CONSTRAINT := RESOURCE_REQUEST + RESOURCE_LIMIT + AFFINITY + ANTI_AFFINITY + PRIORITY + TOPOLOGY + AVAILABILITY
IF RequiredResources unavailable THEN WorkloadState -> PENDING_OR_QUEUED

# ORCHESTRATION
Orchestrator -> manages -> Container + Service + Job + Configuration
ORCHESTRATION_FLOW := DECLARE_DESIRED_STATE -> SCHEDULE -> START -> MONITOR -> RESTART_OR_RESCHEDULE_IF_REQUIRED -> SCALE -> UPDATE
DesiredState -> compared_with -> ActualState
IF ActualState != DesiredState THEN Orchestrator -> RECONCILE
Container -> packages -> APPLICATION + DEPENDENCIES + RUNTIME_CONFIGURATION
Runtime -> executes -> AI_APPLICATION_OR_MODEL

# DATA / MODEL INFRASTRUCTURE
AI_DATA_INFRASTRUCTURE := INGESTION -> VALIDATION -> STORAGE -> TRANSFORMATION -> VERSIONING -> SERVING
DataService -> provides -> Dataset + FEATURE_DATA + RETRIEVAL_DATA
Dataset -> has -> DATA_VERSION + SCHEMA + LOCATION + ACCESS_POLICY
ModelRegistry -> stores -> ModelArtifact
ModelArtifact -> has -> MODEL_VERSION + FORMAT + SIZE + CHECKSUM + METADATA
IF ModelChecksum invalid THEN ModelLoad -> REJECT

# TRAINING INFRASTRUCTURE
TrainingJob -> consumes -> Dataset + ComputeResource + Storage + Network
TRAINING_INFRASTRUCTURE := DATA_LOADING + DISTRIBUTED_COMPUTE + PARAMETER_SYNCHRONISATION + CHECKPOINTING + METRIC_COLLECTION
DISTRIBUTION_STRATEGY := DATA_PARALLEL | MODEL_PARALLEL | PIPELINE_PARALLEL | TENSOR_PARALLEL | HYBRID_PARALLEL
CollectiveCommunication -> exchanges -> GRADIENTS + PARAMETERS + ACTIVATIONS
IF CommunicationTime dominates ComputeTime THEN DistributedEfficiency -> DECREASES
Checkpoint -> stores -> MODEL_STATE + OPTIMISER_STATE + TRAINING_PROGRESS
IF TrainingFailure occurs THEN RecoveryPoint -> MOST_RECENT_VALID_CHECKPOINT

# INFERENCE INFRASTRUCTURE
InferenceService -> serves -> PredictionRequest
Endpoint -> exposes -> InferenceService
INFERENCE_FLOW := REQUEST -> AUTHENTICATE -> VALIDATE -> QUEUE_OR_BATCH -> MODEL_EXECUTION -> POSTPROCESS -> RESPONSE
InferenceService -> constrained_by -> LATENCY + THROUGHPUT + AVAILABILITY + COST
Batching -> groups -> MULTIPLE_REQUESTS
BatchSize -> influences -> THROUGHPUT + LATENCY + MEMORY_USE
LoadBalancer -> distributes -> REQUESTS -> across -> ServiceReplica
IF Replica unavailable THEN LoadBalancer -> ROUTE_TO_HEALTHY_REPLICA

# AUTOSCALING / QUEUES / CACHE
Autoscaler -> adjusts -> REPLICA_COUNT + RESOURCE_ALLOCATION
SCALING_SIGNAL := CPU_UTILISATION | GPU_UTILISATION | QUEUE_DEPTH | REQUEST_RATE | LATENCY | CUSTOM_METRIC
IF Demand increases above_threshold THEN Autoscaler -> SCALE_OUT_OR_UP
Queue -> buffers -> WORK
IF ArrivalRate > ServiceRate THEN QueueDepth -> GENERALLY_INCREASES
QueueingDelay -> contributes_to -> EndToEndLatency
Cache -> stores -> FREQUENTLY_USED_DATA + MODEL_STATE + RETRIEVAL_RESULT
CacheHit -> may_reduce -> LATENCY + BACKEND_LOAD

# OBSERVABILITY / FAILURE / RESILIENCE
OBSERVABILITY := METRICS + LOGS + TRACES + EVENTS
Monitor -> collects -> Metric + Log + Trace
INFRASTRUCTURE_METRIC := CPU_UTILISATION | GPU_UTILISATION | MEMORY_UTILISATION | STORAGE_UTILISATION | NETWORK_THROUGHPUT | LATENCY | REQUEST_RATE | ERROR_RATE | QUEUE_DEPTH | AVAILABILITY | ENERGY_USE
ALERT_FLOW := METRIC_OR_EVENT -> RULE -> ALERT -> ROUTE -> ACKNOWLEDGE -> RESPOND
FAILURE_TYPE := NODE_FAILURE | ACCELERATOR_FAILURE | STORAGE_FAILURE | NETWORK_FAILURE | SERVICE_FAILURE | DATA_PIPELINE_FAILURE | MODEL_LOAD_FAILURE | ORCHESTRATOR_FAILURE
RESILIENCE_MECHANISM := REDUNDANCY | REPLICATION | RETRY | FAILOVER | CHECKPOINT_RECOVERY | RESCHEDULING | BACKUP_RESTORE
IF SinglePointOfFailure exists THEN AvailabilityRisk -> INCREASES

# AVAILABILITY / SECURITY
Availability := UPTIME / TOTAL_TIME
ServiceAvailability -> influenced_by -> FAILURE_RATE + RECOVERY_TIME + REDUNDANCY
RecoveryTimeObjective -> constrains -> RECOVERY_PROCESS
RecoveryPointObjective -> constrains -> ACCEPTABLE_DATA_LOSS_WINDOW
AI_INFRASTRUCTURE_SECURITY := IDENTITY + AUTHENTICATION + AUTHORISATION + NETWORK_SEGMENTATION + ENCRYPTION + SECRET_MANAGEMENT + INTEGRITY_CHECKING + AUDIT_LOGGING
AccessPolicy -> authorises -> ACTION -> on -> RESOURCE
IF Access not_authorised THEN Action -> DENY
MULTI_TENANCY_CONTROL := IDENTITY_ISOLATION + NETWORK_ISOLATION + STORAGE_ISOLATION + RESOURCE_QUOTA
IF Isolation fails THEN SecurityBoundary -> COMPROMISED

# CAPACITY / PERFORMANCE / ENERGY / COST
CAPACITY_PLANNING := FORECAST_DEMAND -> ESTIMATE_RESOURCE_REQUIREMENT -> COMPARE_WITH_CAPACITY -> PROVISION_OR_OPTIMISE
Headroom := TotalCapacity - ExpectedPeakDemand
IF Headroom < RequiredMargin THEN CapacityRisk -> INCREASES
Utilisation := UsedCapacity / AvailableCapacity
IF Utilisation approaches_limit THEN SaturationRisk -> INCREASES
EndToEndLatency := QUEUE_DELAY + NETWORK_DELAY + PREPROCESSING_TIME + MODEL_EXECUTION_TIME + POSTPROCESSING_TIME
Throughput := COMPLETED_WORK / TIME
EnergyUse -> consumed_by -> ComputeResource + Storage + Network + Cooling
EnergyEfficiency := USEFUL_AI_WORK / ENERGY_CONSUMED
INFRASTRUCTURE_COST := COMPUTE_COST + STORAGE_COST + NETWORK_COST + LICENSING_COST + OPERATIONS_COST + ENERGY_COST
CostOptimisation -> balances -> PERFORMANCE + AVAILABILITY + CAPACITY + COST

# CONFIGURATION / CHANGE / PROVENANCE
Configuration -> defines -> RESOURCE_SETTINGS + NETWORK_SETTINGS + STORAGE_SETTINGS + RUNTIME_SETTINGS + SECURITY_SETTINGS
InfrastructureAsCode -> represents -> DESIRED_INFRASTRUCTURE_CONFIGURATION
IF RuntimeConfiguration != ApprovedConfiguration THEN ConfigurationDrift -> TRUE
CHANGE_FLOW := REQUEST -> IMPACT_ANALYSIS -> APPROVAL -> TEST -> DEPLOY -> VERIFY -> MONITOR
IF Change affects_critical_service THEN RollbackPlan -> REQUIRED_OR_DEFINED
Provenance -> records -> RESOURCE_ID + CONFIGURATION_VERSION + SOFTWARE_VERSION + MODEL_VERSION + DATA_VERSION + DEPLOYMENT + CHANGE + TIMESTAMP
AIInfrastructure -> linked_to -> Provenance

# PERFORMANCE / REASONING
AI_INFRASTRUCTURE_PERFORMANCE := LATENCY + THROUGHPUT + AVAILABILITY + UTILISATION + SCALABILITY + RELIABILITY + COST_EFFICIENCY + ENERGY_EFFICIENCY
AIInfrastructure -> evaluated_by -> AI_INFRASTRUCTURE_PERFORMANCE
AI_INFRASTRUCTURE_REASONING := DEFINE_AI_WORKLOAD -> ESTIMATE_COMPUTE_MEMORY_STORAGE_NETWORK -> SELECT_ACCELERATORS -> DESIGN_CLUSTER -> DESIGN_STORAGE_AND_DATA_PATHS -> DESIGN_NETWORK_AND_INTERCONNECT -> CONFIGURE_RUNTIME -> DEFINE_SCHEDULING -> DEFINE_TRAINING_OR_INFERENCE_SERVICES -> DEFINE_SCALING -> DEFINE_OBSERVABILITY -> DEFINE_SECURITY -> DEFINE_RESILIENCE -> PLAN_CAPACITY -> DEPLOY -> MONITOR -> HANDLE_FAILURES -> OPTIMISE_PERFORMANCE_COST_ENERGY -> CONTROL_CHANGES -> PRESERVE_PROVENANCE

# INFERENCE RULES
IF WorkloadDemand increases THEN RequiredCapacity -> MAY_INCREASE
IF AcceleratorMemory insufficient THEN PlacementStrategy -> REASSESS
IF NetworkLatency increases THEN DistributedWorkloadPerformance -> MAY_DECREASE
IF QueueDepth increases persistently THEN CapacityOrServiceRate -> REASSESS
IF Utilisation approaches_limit THEN SaturationRisk -> INCREASES
IF Failure occurs AND redundancy available THEN Failover -> MAY_RESTORE_SERVICE
IF ConfigurationDrift detected THEN ComplianceState -> REASSESS
IF CriticalMetric exceeds_threshold THEN Alert -> TRIGGER

# SEMANTIC RELATIONSHIPS
RELATION hosts(AIInfrastructure, AIWorkload)
RELATION executes(ComputeResource, AIWorkload)
RELATION stores(Storage, ModelArtifact)
RELATION transports(Network, AIData)
RELATION contains(Cluster, Node)
RELATION assigns(Scheduler, AIWorkload, ComputeResource)
RELATION manages(Orchestrator, Workload)
RELATION stores(ModelRegistry, ModelArtifact)
RELATION consumes(TrainingJob, ComputeResource)
RELATION serves(InferenceService, PredictionRequest)
RELATION distributes(LoadBalancer, Request)
RELATION adjusts(Autoscaler, ResourceAllocation)
RELATION observes(Monitor, AIInfrastructure)
RELATION protects(SecurityControl, InfrastructureResource)
RELATION records(Provenance, AIInfrastructure)

# SEMANTIC EQUIVALENCE
SEMANTIC_EQUIVALENCE:
  "AI infrastructure" ~= "artificial intelligence infrastructure"
  "AI accelerator" ~= "machine learning accelerator"
  "GPU cluster" ~= "accelerator compute cluster"
  "distributed training" ~= "multi-node or multi-accelerator training"
  "inference serving" ~= "model serving"
  "model registry" ~= "AI model artifact registry"
  "autoscaling" ~= "automatic resource scaling"
  "observability" ~= "metrics logs traces and events"
  "configuration drift" ~= "runtime divergence from approved configuration"
  "infrastructure as code" ~= "IaC"

# CONFIDENCE
CONFIDENCE:
  ontology_domain: 0.99
  compute_accelerator_mapping: 0.99
  storage_network_mapping: 0.99
  cluster_orchestration_mapping: 0.99
  training_infrastructure_mapping: 0.99
  inference_infrastructure_mapping: 0.99
  observability_resilience_mapping: 0.99
  security_mapping: 0.99
  capacity_energy_mapping: 0.99
  semantic_relationships: 0.99

# UPDATE MODEL
UPDATE_MODEL:
  ON new_ai_workload:
    MAP workload_profile
    MAP compute_requirement
    MAP memory_requirement
    MAP storage_requirement
    MAP network_requirement
    UPDATE capacity_plan
    UPDATE confidence
    RECOMPUTE FLOW_HASH
  ON infrastructure_change:
    UPDATE configuration
    UPDATE capacity
    UPDATE topology
    REASSESS performance
    REASSESS resilience
    PRESERVE provenance
  ON operational_failure:
    UPDATE health_state
    UPDATE availability
    EXECUTE recovery_if_defined
    UPDATE alerts
    PRESERVE provenance
  ON scaling_event:
    UPDATE replica_or_resource_count
    UPDATE utilisation
    UPDATE cost_state
    UPDATE capacity_state

# AI CONSUMPTION
AI_CONSUMPTION:
  parse_as: FORMAL_AI_INFRASTRUCTURE_ONTOLOGY
  reasoning_mode: DISTRIBUTED_COMPUTE_INFRASTRUCTURE_RELATIONAL
  preserve: [typed_entities, workload_profiles, resource_identity, topology, configurations, model_and_data_versions, capacity_state, monitoring_state, security_controls, failure_history, confidence, provenance]
  infer: [resource_requirements, scheduling_dependencies, accelerator_placement, network_bottleneck_risk, scaling_requirements, failure_recovery_paths, capacity_risk, configuration_compliance]
  prohibit: [invented_resource_metrics, assumed_capacity, assumed_availability, assumed_security, fabricated_failure_recovery, missing_provenance_presented_as_complete]

# FLOW HASH
FLOW_HASH_INPUT := DOMAIN + ONTOLOGY + CORE_ENTITIES + INFRASTRUCTURE_WORKLOAD + COMPUTE_MEMORY_STORAGE + NETWORK_CLUSTER_SCHEDULING + ORCHESTRATION + DATA_MODEL_INFRASTRUCTURE + TRAINING_INFRASTRUCTURE + INFERENCE_INFRASTRUCTURE + AUTOSCALING_QUEUES_CACHE + OBSERVABILITY_FAILURE_RESILIENCE + AVAILABILITY_SECURITY + CAPACITY_PERFORMANCE_ENERGY_COST + CONFIGURATION_CHANGE_PROVENANCE + REASONING + INFERENCE_RULES + SEMANTIC_RELATIONSHIPS + SEMANTIC_EQUIVALENCE + CONFIDENCE + UPDATE_MODEL + AI_CONSUMPTION
FLOW_HASH := HASH(FLOW_HASH_INPUT)

END FLOW