Cluster Computing Ontology

FLOW ALGEBRA
Cluster Computing Ontology
Kireth Semantic Research Archive

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

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "ClusterComputingOntology" v4.0

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

# ---------- PRIMARY ENTITY ----------
ENTITY ClusterComputing {
    definition:
        "A distributed computing model in which multiple interconnected
         computing nodes operate together as a coordinated system to provide
         shared computation, services, capacity, availability or performance.";

    objectives: [
        ParallelProcessing,
        Scalability,
        HighAvailability,
        LoadDistribution,
        ResourceSharing,
        FaultTolerance
    ];
}

# ---------- CORE ENTITIES ----------
Cluster := {
    ComputeNode,
    HeadNode,
    WorkerNode,
    StorageNode,
    ManagementNode,
    Network
};

ClusterResource := {
    CPU,
    Memory,
    Storage,
    Accelerator,
    NetworkBandwidth
};

ClusterWorkload := {
    Job,
    Task,
    Process,
    Service,
    BatchWorkload,
    ParallelWorkload
};

ClusterProperty := {
    Scalability,
    Availability,
    Throughput,
    Performance,
    FaultTolerance,
    ResourceUtilisation
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
Cluster CONSISTS_OF ComputeNode;
ComputeNode CONNECTS_TO ClusterNetwork;
ComputeNode PROVIDES ClusterResource;
Cluster EXECUTES ClusterWorkload;
Scheduler ASSIGNS Workload TO ComputeNode;
Cluster MAY_SHARE Storage;
Cluster MAY_PROVIDE Service;

# ---------- CLUSTER TOPOLOGY ----------
ClusterTopology :=
    Nodes
    + Interconnect
    + SharedOrDistributedStorage
    + ManagementLayer;

NodeSet := SET_OF(ComputeNode);

IF cardinality(NodeSet) > 1
AND NodesCoordinated
THEN ClusterSystem := true;

# ---------- NODE MODEL ----------
ComputeNode :=
    {
        Processor,
        Memory,
        NetworkInterface,
        LocalStorage,
        OperatingEnvironment
    };

NodeState := {
    Available,
    Busy,
    Draining,
    Unreachable,
    Failed,
    Maintenance
};

IF NodeState == Available
THEN Scheduler MAY_ASSIGN Workload;

# ---------- HEAD / MANAGEMENT NODE ----------
HeadNode MAY_COORDINATE {
    JobSubmission,
    Scheduling,
    ResourceManagement,
    Monitoring
};

ManagementNode MANAGES ClusterState;

WorkerNode EXECUTES AssignedWorkload;

# ---------- JOB MODEL ----------
Job :=
    {
        JobIdentifier,
        ResourceRequest,
        TaskSet,
        Priority,
        State
    };

JobState := {
    Queued,
    Scheduled,
    Running,
    Completed,
    Failed,
    Cancelled
};

Job ->
    Submit ->
    Queue ->
    Schedule ->
    AllocateResources ->
    Execute ->
    CompleteOrFail;

# ---------- TASK DISTRIBUTION ----------
ParallelJob ->
    DecomposeIntoTasks ->
    AssignTasksToNodes ->
    ExecuteConcurrently ->
    Synchronise ->
    AggregateResult;

Task MAY_DEPEND_ON OtherTask;

IF TasksIndependent
THEN ParallelismPotential := HIGH;

# ---------- SCHEDULING ----------
Scheduler :=
    ResourceManager
    + SchedulingPolicy;

SchedulingPolicy MAY_INCLUDE {
    FirstComeFirstServed,
    PriorityScheduling,
    FairShare,
    Backfilling,
    ResourceAwareScheduling
};

Scheduler MATCHES ResourceRequest TO AvailableResources;

IF RequestedResources AVAILABLE
THEN Job MAY_BE Scheduled;

# ---------- RESOURCE ALLOCATION ----------
ResourceRequest :=
    {
        CPUCount,
        MemoryAmount,
        StorageRequirement,
        AcceleratorRequirement,
        TimeRequirement
    };

ResourceAllocation :=
    ReserveResources
    -> AssignNode
    -> LaunchWorkload
    -> ReleaseResources;

# ---------- PARALLEL COMPUTING ----------
Parallelism := {
    TaskParallelism,
    DataParallelism
};

TaskParallelism EXECUTES DifferentTasksConcurrently;
DataParallelism PROCESSES DataPartitionsConcurrently;

ClusterComputing SUPPORTS ParallelExecution;

Speedup :=
    SequentialExecutionTime
    / ParallelExecutionTime;

# ---------- INTERCONNECT ----------
ClusterNetwork CONNECTS ComputeNode;

InterconnectProperty := {
    Bandwidth,
    Latency,
    Reliability,
    Topology
};

CommunicationPerformance :=
    f(
        Bandwidth,
        Latency,
        MessagePattern,
        DataVolume
    );

IF CommunicationOverhead HIGH
THEN ParallelEfficiency := DECREASE;

# ---------- STORAGE ----------
ClusterStorage := {
    LocalStorage,
    SharedStorage,
    DistributedStorage
};

SharedStorage ACCESSIBLE_BY MultipleNodes;
DistributedStorage DISTRIBUTES DataAcrossNodes;

WorkloadDataLocality MAY_AFFECT Performance;

IF DataNearCompute
THEN DataTransferCost MAY_DECREASE;

# ---------- LOAD BALANCING ----------
ClusterLoad :=
    aggregate(ResourceUsageAcrossNodes);

LoadBalancer OR Scheduler MAY_DISTRIBUTE Workload;

LoadBalancingGoal :=
    minimise(NodeOverload)
    + maximise(ResourceUtilisation);

IF NodeOverloaded
AND OtherNodeAvailable
THEN WorkloadRedistribution MAY_BE Considered;

# ---------- HIGH AVAILABILITY ----------
HighAvailabilityCluster :=
    RedundantNodes
    + FailureDetection
    + Failover;

Service MAY_HAVE MultipleInstances;

IF ActiveNodeFails
AND StandbyOrReplicaAvailable
THEN Failover MAY_RESTORE Service;

# ---------- FAILURE MODEL ----------
ClusterFailure := {
    NodeFailure,
    ProcessFailure,
    StorageFailure,
    NetworkFailure,
    ManagementFailure
};

FailureDetector MAY_USE {
    Heartbeat,
    HealthCheck,
    Timeout,
    MonitoringAgent
};

IF NodeFailureDetected
THEN {
    StopNewSchedulingToNode;
    RequeueRecoverableWorkload;
    RestoreRequiredRedundancy;
};

# ---------- FAULT TOLERANCE ----------
ClusterFaultTolerance MAY_USE {
    Redundancy,
    Replication,
    Checkpointing,
    JobRestart,
    Failover,
    WorkloadRescheduling
};

Checkpoint SAVES ExecutionState;

IF WorkloadFails
AND ValidCheckpointExists
THEN Workload MAY_RESUME_FROM Checkpoint;

# ---------- SCALABILITY ----------
ClusterScalability :=
    AbilityToIncreaseCapacityByAddingNodes;

ScaleOut ->
    AddComputeNode
    -> RegisterResources
    -> UpdateScheduler
    -> IncreaseAvailableCapacity;

IF AddedNodesIncreaseUsefulThroughput
THEN ScalabilityEvidence := INCREASE;

# ---------- CLUSTER TYPES ----------
ClusterType := {
    HighPerformanceCluster,
    HighAvailabilityCluster,
    LoadBalancingCluster,
    ComputeCluster,
    StorageCluster
};

HighPerformanceCluster PRIORITISES ComputationalPerformance;
HighAvailabilityCluster PRIORITISES ServiceContinuity;
LoadBalancingCluster PRIORITISES RequestDistribution;

# ---------- OBSERVABILITY ----------
ClusterObservability := {
    NodeMetrics,
    JobMetrics,
    ResourceMetrics,
    Logs,
    HealthState
};

MonitoringSystem OBSERVES Cluster;

IF ResourceUtilisationNearLimit
THEN CapacityAlert MAY_BE Generated;

# ---------- SECURITY ----------
ClusterSecurity MAY_INCLUDE {
    NodeAuthentication,
    UserAuthentication,
    Authorisation,
    NetworkProtection,
    WorkloadIsolation,
    DataProtection
};

User SUBMITS Job;
Authorisation DETERMINES AllowedClusterResources;

# ---------- SEMANTIC REASONING ----------
IF MultipleConnectedNodes
AND NodesManagedAsCoordinatedComputeResource
THEN ClusterComputingClassification := supported;

IF WorkloadSplitAcrossNodes
THEN ParallelExecution := true;

IF FailedNodeWorkloadReassigned
THEN ClusterFaultToleranceEvidence := increase;

IF ClusterAddsNodesToIncreaseCapacity
THEN ScalingModel := HorizontalScaling;

IF SchedulerCannotMeetResourceRequest
THEN JobState := Queued;

IF ClusterHasRedundantServiceInstances
AND FailoverAvailable
THEN AvailabilityPotential := increase;

IF CommunicationCostDominatesComputation
THEN ParallelEfficiency := decrease;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "cluster computing" ~= "computing cluster";
    "compute cluster" ~= "cluster computing system";
    "worker node" ~= "compute worker";
    "head node" ~= "cluster head node";
    "cluster scheduler" ~= "job scheduler" WHEN context == cluster;
    "scale out" ~= "horizontal cluster scaling";
    "high availability cluster" ~= "HA cluster";
    "high performance computing cluster" ~= "HPC cluster";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_CLUSTER(q) ->
    IDENTIFY_NODES(q) ->
    IDENTIFY_RESOURCES(q) ->
    IDENTIFY_WORKLOAD(q) ->
    IDENTIFY_SCHEDULER(q) ->
    IDENTIFY_FAILURE_MODEL(q) ->
    IDENTIFY_CLUSTER_PROPERTY(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        cluster_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: ClusterComputing;
    ontology: ClusterComputingOntology;
    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:
        validate
        -> classify
        -> identify_resources
        -> register_cluster_relationships
        -> integrate;

    new_workload:
        classify
        -> identify_resource_requirements
        -> identify_dependencies
        -> map_scheduler_relationship
        -> integrate;

    new_failure:
        classify
        -> identify_affected_node
        -> identify_affected_workloads
        -> determine_recovery
        -> integrate;

    cluster_revision:
        recompute_resources
        -> recompute_topology
        -> recompute_capacity
        -> recompute_confidence
        -> regenerate_flow_hash;

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

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + Cluster
    + ClusterResource
    + ClusterWorkload
    + ClusterProperty
    + ClusterType
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
ClusterComputingOntology
    -> IdentifyClusterNodes
    -> RegisterResources
    -> ReceiveWorkload
    -> DetermineResourceRequirements
    -> ScheduleWork
    -> AllocateNodes
    -> ExecuteTasks
    -> CoordinateParallelProcessing
    -> MonitorClusterState
    -> DetectFailures
    -> RecoverOrRescheduleWork
    -> ScaleClusterCapacity
    -> MaintainClusterService;

END FLOW