Distributed Computing Ontology

FLOW ALGEBRA
Distributed Computing Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/distributed-computing/distributed-computing-ontology-2.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "DistributedComputingOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := DistributedComputing
ONTOLOGY := DistributedComputingOntology

# ---------- PRIMARY ENTITY ----------
ENTITY DistributedComputing {
    definition:
        "A computational model in which autonomous computing components
         located across multiple nodes communicate and coordinate to execute
         tasks, manage distributed state, share resources or collectively
         provide services.";

    properties: [
        Distribution,
        Concurrency,
        Communication,
        Coordination,
        Scalability,
        FaultTolerance
    ];
}

# ---------- CORE ENTITIES ----------
DistributedSystem := {
    Node,
    Process,
    Service,
    Message,
    Resource,
    Network,
    DistributedState
};

Node := {
    ClientNode,
    ServerNode,
    PeerNode,
    WorkerNode,
    CoordinatorNode,
    ReplicaNode
};

Process := ComputationalExecutionUnit;

DistributedResource := {
    Data,
    Storage,
    Compute,
    Service,
    Queue,
    Lock
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
DistributedSystem CONSISTS_OF Node;
Node HOSTS Process;
Process EXECUTES Task;
Process COMMUNICATES_WITH Process;
Node COMMUNICATES_WITH Node;
Communication USES Network;
Process SENDS Message;
Process RECEIVES Message;
DistributedSystem SHARES DistributedResource;
DistributedSystem MAINTAINS DistributedState;

# ---------- DISTRIBUTED TASK ----------
DistributedTask :=
    TaskDecomposition
    + TaskAssignment
    + DistributedExecution
    + Coordination
    + ResultAggregation;

Task ->
    Decompose ->
    SubtaskSet ->
    AssignToNodes ->
    Execute ->
    Coordinate ->
    Aggregate;

IF SubtasksIndependent
THEN ParallelExecution MAY_BE_USED;

# ---------- COMMUNICATION ----------
CommunicationModel := {
    MessagePassing,
    RequestResponse,
    RemoteInvocation,
    PublishSubscribe,
    EventStreaming
};

Message :=
    {
        Sender,
        Receiver,
        Payload,
        Identifier,
        Timestamp,
        Metadata
    };

Sender SENDS Message;
Receiver RECEIVES Message;

MessageTransmission MAY_HAVE {
    Delay,
    Loss,
    Duplication,
    Reordering
};

# ---------- SYNCHRONY ----------
CommunicationTiming := {
    Synchronous,
    Asynchronous
};

SynchronousCommunication MAY_BLOCK WaitingProcess;
AsynchronousCommunication ALLOWS TemporalDecoupling;

DistributedSystem MAY_HAVE UncertainMessageDelay;

# ---------- REMOTE INVOCATION ----------
RemoteInvocation :=
    Caller
    -> EncodeRequest
    -> SendRequest
    -> RemoteExecution
    -> EncodeResponse
    -> ReturnResponse;

RemoteInvocation MAY_FAIL_DUE_TO {
    CallerFailure,
    RemoteFailure,
    NetworkFailure,
    Timeout
};

IF TimeoutOccurs
THEN RemoteExecutionOutcome MAY_BE Unknown;

# ---------- DISTRIBUTED STATE ----------
DistributedState :=
    StateStoredOrMaintainedAcross MultipleNodes;

Replica := CopyOfDistributedState;

Replication CREATES Replica;
Replica INCREASES Redundancy;
Replica MAY_INCREASE Availability;
Replication REQUIRES ConsistencyStrategy;

StateUpdate ->
    ApplyLocally ->
    Propagate ->
    ApplyRemotely ->
    ConvergeOrConfirm;

# ---------- CONSISTENCY ----------
ConsistencyModel := {
    StrongConsistency,
    SequentialConsistency,
    CausalConsistency,
    EventualConsistency
};

ConsistencyModel DEFINES {
    UpdateVisibility,
    OperationOrdering,
    ReplicaBehaviour
};

StrongConsistency PRESENTS SingleCurrentLogicalState;
EventualConsistency ALLOWS TemporaryReplicaDivergence;

IF UpdatesStop
AND ReplicationContinues
THEN EventualConsistency EXPECTS ReplicaConvergence;

# ---------- EVENT ORDERING ----------
DistributedEvent := EventAt(NodeOrProcess);

DistributedSystem HAS_NO_INHERENT PerfectGlobalClock;

OrderingMechanism := {
    PhysicalClock,
    LogicalClock,
    VectorClock,
    SequenceNumber
};

HappensBefore(A, B) :=
    A CAUSALLY_PRECEDES B;

IF NOT HappensBefore(A,B)
AND NOT HappensBefore(B,A)
THEN A AND B MAY_BE Concurrent;

# ---------- CONCURRENCY ----------
Concurrency :=
    MultipleProcessesOrNodes
    EXECUTE DuringOverlappingIntervals;

ConcurrencyIssue := {
    RaceCondition,
    ConflictingUpdate,
    LostUpdate,
    OrderingViolation
};

CoordinationControl MAY_INCLUDE {
    Lock,
    Transaction,
    AtomicOperation,
    Consensus,
    Versioning
};

# ---------- CONSENSUS ----------
Consensus :=
    DistributedParticipants
    -> Propose
    -> ExchangeInformation
    -> FollowAgreementProtocol
    -> Decide;

ConsensusProperty := {
    Agreement,
    Validity,
    Termination
};

Consensus SUPPORTS {
    ReplicatedStateMachine,
    LeaderElection,
    DistributedCoordination
};

# ---------- LEADER ELECTION ----------
LeaderElection :=
    CandidateNodes
    -> ElectionProtocol
    -> SelectLeader;

Leader COORDINATES CertainDistributedOperations;

IF LeaderUnavailable
THEN LeaderElection MAY_RESTART;

# ---------- DISTRIBUTED FAILURE ----------
FailureType := {
    ProcessFailure,
    NodeFailure,
    NetworkFailure,
    MessageLoss,
    MessageDelay,
    NetworkPartition,
    ByzantineFailure
};

PartialFailure :=
    FailureOfSubsetOfComponents
    WHILE OtherComponentsContinue;

DistributedSystem MUST_HANDLE PartialFailure;

# ---------- FAULT TOLERANCE ----------
FaultTolerance :=
    Detect
    -> Isolate
    -> ContinueOrFailSafely
    -> Recover
    -> RestoreRedundancy;

FaultTolerance MAY_USE {
    Replication,
    Redundancy,
    Retry,
    Failover,
    Checkpoint,
    Recovery
};

IF OperationRetried
THEN Idempotency SHOULD_BE Considered;

# ---------- NETWORK PARTITION ----------
NetworkPartition :=
    LossOrSevereFailureOfCommunication
    BETWEEN GroupsOfNodes;

PartitionTolerance :=
    AbilityToContinueDefinedOperationDuringPartition;

IF PartitionOccurs
THEN ConsistencyAvailabilityTradeoff MAY_APPLY;

# ---------- CAP MODEL ----------
CAP := {
    Consistency,
    Availability,
    PartitionTolerance
};

UnderNetworkPartition:
    StrongConsistency AND FullAvailability
    CANNOT_ALWAYS_BE_SIMULTANEOUSLY_GUARANTEED;

# ---------- SCALABILITY ----------
Scalability :=
    AbilityToMaintainUsefulPerformance
    AsLoadOrSystemSizeIncreases;

Scaling := {
    VerticalScaling,
    HorizontalScaling
};

HorizontalScaling ->
    AddNodes
    -> RedistributeWork
    -> IncreaseDistributedCapacity;

# ---------- LOAD BALANCING ----------
LoadBalancer DISTRIBUTES Workload ACROSS NodesOrServiceInstances;

LoadBalancingStrategy := {
    RoundRobin,
    LeastConnections,
    LeastLoaded,
    HashBased,
    LocalityAware
};

IF LoadImbalanceDetected
THEN Rebalancing MAY_BE_REQUIRED;

# ---------- DATA PARTITIONING ----------
DataPartitioning :=
    DivideData
    -> AssignPartitions
    -> LocatePartitions
    -> RouteOperations;

Shard := DataPartition;

Sharding DISTRIBUTES Shard ACROSS Nodes;

PartitioningStrategy MAY_INCLUDE {
    RangePartitioning,
    HashPartitioning,
    DirectoryPartitioning
};

# ---------- DISTRIBUTED TRANSACTION ----------
DistributedTransaction :=
    TransactionSpanning MultipleNodesOrResources;

TransactionProperty := {
    Atomicity,
    Consistency,
    Isolation,
    Durability
};

CommitProtocol COORDINATES DistributedTransaction;

FailureDuringCommit MAY_REQUIRE RecoveryOrResolution;

# ---------- SERVICE DISCOVERY ----------
DistributedService HAS ServiceInstance;
ServiceInstance RUNS_ON Node;

ServiceDiscovery MAPS ServiceIdentity TO AvailableServiceInstance;

Client ->
    DiscoverService ->
    SelectInstance ->
    CommunicateWithInstance;

# ---------- ARCHITECTURAL MODELS ----------
DistributedArchitecture := {
    ClientServer,
    PeerToPeer,
    Cluster,
    Grid,
    ServiceOriented,
    Microservices,
    CloudDistributedSystem
};

ClientServer DISTINGUISHES ClientRole FROM ServerRole;
PeerToPeer ALLOWS PeerNodes TO ActAsClientAndServer;
Microservices DISTRIBUTES ApplicationFunctionsAcrossServices;

# ---------- OBSERVABILITY ----------
DistributedObservability := {
    Logs,
    Metrics,
    Traces,
    HealthSignals
};

Trace CONNECTS RelatedDistributedOperations;
CorrelationIdentifier LINKS RelatedEvents;

Observability SUPPORTS {
    FailureDiagnosis,
    PerformanceAnalysis,
    DependencyAnalysis
};

# ---------- SECURITY ----------
DistributedSecurityControl := {
    Authentication,
    Authorisation,
    Encryption,
    IntegrityProtection,
    SecureChannel,
    IdentityManagement
};

DistributedSecurityControl PROTECTS DistributedInteraction;

# ---------- SEMANTIC REASONING ----------
IF ComputingSystem HAS MultipleAutonomousNodes
AND Nodes CommunicateToCoordinateWork
THEN DistributedSystemClassification := supported;

IF State EXISTS_ON MultipleReplicas
THEN ConsistencyModel REQUIRED;

IF CommunicationCanFailIndependentlyOfNodes
THEN PartialFailureModel REQUIRED;

IF NetworkPartitionPossible
THEN PartitionBehaviour REQUIRED;

IF RequestRetried
AND OperationNonIdempotent
THEN DuplicateSideEffectRisk := INCREASE;

IF ReplicationFactor > 1
THEN Redundancy := INCREASE;

IF FailedNodeReplacedWithoutServiceLoss
THEN FaultToleranceEvidence := INCREASE;

IF AdditionalNodesIncreaseCapacity
THEN HorizontalScalabilityEvidence := INCREASE;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "distributed computing" ~= "distributed computation";
    "distributed system" ~= "distributed computing system";
    "distributed node" ~= "node" WHEN context == distributed_computing;
    "replicated state" ~= "distributed replicated state";
    "scale out" ~= "horizontal scaling";
    "scale up" ~= "vertical scaling";
    "network partition" ~= "communication partition";
    "distributed consensus" ~= "consensus protocol process";
    "fault tolerance" ~= "distributed failure resilience";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_NODES(q) ->
    IDENTIFY_PROCESSES(q) ->
    IDENTIFY_COMMUNICATION(q) ->
    IDENTIFY_DISTRIBUTED_STATE(q) ->
    IDENTIFY_CONSISTENCY(q) ->
    IDENTIFY_FAILURE_MODEL(q) ->
    IDENTIFY_COORDINATION(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        distributed_system_consistency,
        semantic_consistency,
        evidence_strength,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: DistributedComputing;
    ontology: DistributedComputingOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_node_type:
        validate
        -> classify
        -> identify_roles
        -> relate
        -> integrate;

    new_communication_model:
        validate
        -> identify_message_semantics
        -> identify_failure_modes
        -> integrate;

    new_consistency_model:
        validate
        -> identify_visibility_rules
        -> identify_ordering_rules
        -> identify_replication_behaviour
        -> integrate;

    new_failure:
        classify
        -> identify_affected_components
        -> update_system_state
        -> identify_recovery
        -> integrate;

    revision:
        recompute_relationships
        -> recompute_inferences
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + DistributedSystem
    + Node
    + CommunicationModel
    + ConsistencyModel
    + FailureType
    + DistributedArchitecture
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
DistributedComputingOntology
    -> IdentifyDistributedNodes
    -> IdentifyProcesses
    -> EstablishCommunication
    -> DistributeTasks
    -> CoordinateExecution
    -> MaintainDistributedState
    -> DefineConsistency
    -> ManageConcurrency
    -> DetectPartialFailure
    -> ApplyFaultTolerance
    -> ScaleDistributedResources
    -> MaintainDistributedService;

END FLOW