Package jline.solvers.nc

Class SolverNC

java.lang.Object

jline.solvers.Solver

jline.solvers.NetworkSolver

jline.solvers.nc.SolverNC

Direct Known Subclasses:

NC

public class SolverNC
extends NetworkSolver

Solver for Normalizing Constant (NC) method applied to closed queueing networks.

SolverNC implements the normalizing constant approach for computing exact performance measures of closed product-form queueing networks. The normalizing constant G(N) represents the partition function that ensures steady-state probabilities sum to one.

Key NC solver capabilities:

• Exact normalizing constant computation
• Convolution algorithm implementation
• Load-dependent service station support
• Cache-enabled queueing network analysis
• Joint and marginal probability computation
• State probability aggregation methods

The solver supports various computation methods including standard convolution, tree convolution, and specialized algorithms for cache networks and load-dependent stations. Results include exact performance metrics and state probabilities.

Since:

1.0

See Also:

• jline.api.pfqn.nc
• NCResult
• NCOptions

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	SolverNC.NormalizingConstantResult
static class	SolverNC.SolverNCJointReturn
static class	SolverNC.SolverNCLDReturn
static class	SolverNC.SolverNCMargReturn
static class	SolverNC.SolverNCReturn

Nested classes/interfaces inherited from class jline.solvers.NetworkSolver

NetworkSolver.SolverConfigurator

Field Summary

Fields inherited from class jline.solvers.NetworkSolver

avgHandles, lastPerctMethod, model, sn, tranHandles

Fields inherited from class jline.solvers.Solver

enableChecks, name, options, random, result

Constructor Summary

Constructors

Constructor	Description
SolverNC(Network model)
SolverNC(Network model, Object... varargin)
SolverNC(Network model, String method)
SolverNC(Network model, SolverOptions options)

Method Summary

Modifier and Type	Method	Description
static SolverOptions	defaultOptions()
Ret.DistributionResult	getCdfRespT()	Get cumulative distribution function of response times with default parameters
Matrix	getCdfRespT(AvgHandle... R)	Get cumulative distribution function of response times at FCFS and delay nodes
static FeatureSet	getFeatureSet()	Returns the feature set supported by the NC solver
SolverNC.NormalizingConstantResult	getNormalizingConstant()	Get the normalizing constant and its logarithm
Double	getProb(Node node, Matrix state)
Double	getProbAggr(Node node)	Get aggregated probability for a specific node using current state
Double	getProbAggr(Node node, Matrix state_a)	Get aggregated probability for a specific node and state
Ret.ProbabilityResult	getProbMarg(int ist, int jobclass, Matrix state_m)	Marginal queue-length distribution at a station, routing the CLI's (station, class) call to the exact procomom/enumeration path in getProbMarg(Node).
Ret.ProbabilityResult	getProbMarg(Node node)
Ret.ProbabilityResult	getProbNormConstAggr()	Get the log normalization constant for aggregated probabilities
Ret.ProbabilityResult	getProbSys()	Get system-wide joint probability
Ret.ProbabilityResult	getProbSysAggr()	Get aggregated system-wide joint probability
NetworkStruct	getStruct()
static boolean	hasRetrievalCache(NetworkStruct sn)	True if the model contains a Cache equipped with a delayed-hit retrieval system.
boolean	isStochasticMethod(String method)	NC is deterministic except for the Monte Carlo integration methods (mci/imci), logistic sampling (ls), and the sampling method, whose estimates depend on the random seed.
String[]	listValidMethods()	List all valid solution methods for this solver
NCResult	meClosed()	Maximum Entropy algorithm for Closed Queueing Networks (Kouvatsos 1994, Section 3.3): pseudo-open decomposition followed by a convolution over the population lattice, iterated on the flow (work rate) equations.
NCResult	meClosed(MeOqnOptions meOptions)	Maximum Entropy algorithm for Closed Queueing Networks with custom options (Kouvatsos 1994, Section 3.3).
static double	memBufferSize(NetworkStruct sn, int ist)	Physical buffer size of a station in jobs, in service included: the tighter of the station capacity and the sum of the per-class capacities, infinite when the station is unbounded.
static DropStrategy	memDropRule(NetworkStruct sn, int ist)	Drop rule declared at a station for the first class, defaulting to Drop, which is what refreshCapacity assigns to a finite buffer reachable by an open class.
static boolean	memFiniteBufferPath(NetworkStruct sn, SolverOptions options)	True when method='mem' has been requested on a model whose finite buffers MEM carries explicitly.
static boolean	memHasFiniteBuffers(NetworkStruct sn)	True when the model is a single-class open network carrying a finite buffer that MEM represents explicitly, as a censored GE/GE/c/0;N queue.
NCResult	meMixed()	Maximum Entropy algorithm for Mixed Queueing Networks: composition of the open (Section 3.2) and closed (Section 3.3) algorithms with product-form-style conditioning.
NCResult	meMixed(MeOqnOptions meOptions)	Maximum Entropy algorithm for Mixed Queueing Networks with custom options.
static String	memUnsupportedReason(NetworkStruct sn)	Returns null when the Maximum Entropy Method (Kouvatsos 1994) supports the features of the model described by sn (node types, class switching, scheduling, source presence); otherwise a message describing the first unsupported feature.
static String	memUnsupportedReason(NetworkStruct sn, boolean closed)	Returns null when the Maximum Entropy Method (Kouvatsos 1994) supports the features of the model described by sn; otherwise a message describing the first unsupported feature.
static String	memUnsupportedReason(NetworkStruct sn, boolean hasOpen, boolean hasClosed)	Feature check for the Maximum Entropy Method given the class composition of the model: hasOpen/hasClosed flag the presence of open and closed classes (both true for mixed models).
NCResult	meOpen()	Maximum Entropy algorithm for Open Queueing Networks.
NCResult	meOpen(MeOqnOptions meOptions)	Maximum Entropy algorithm for Open Queueing Networks with custom options.
String	resolveMethod(SolverOptions options)	Feature-driven resolution of method='default': an open network with non-Markovian (non-unit SCV) variability within the MEM feature set is solved by the Maximum Entropy Method by default, since the normalizing-constant path would silently exponentialize it.
void	runAnalyzer()	Executes the solver algorithm to analyze the model.
Object	sample(Node node)	Sample node state trajectory with default number of samples
Object	sample(Node node, int numEvents)	Sample node state trajectory
void	setStruct(NetworkStruct sn)
boolean	supports(Network model)	Checks whether the given model is supported by the NC solver
boolean	supportsExactSensitivity()	The normalizing-constant solver is exact on the same product-form class that pfqn_sens differentiates, so getSensitivityTable uses the analytic branch.
static boolean	supportsModel(Network model)	Static method to check whether the given model is supported by the NC solver.
String	supportsModelMethod(String method)	Method-aware gate.

Methods inherited from class jline.solvers.NetworkSolver

aCaT, aCT, aCT, aCT, aCT, aCT, aCT, aIT, aLT, aNCT, aNCT, aNCT, aNCT, aNCT, aNCT, aNT, aNT, aNT, aNT, aNT, aNT, aOT, aRLT, aST, aST, aST, aT, aT, aT, aT, aT, aT, avg, avg, avg, avgArvR, avgArvRChain, avgArvRHandles, avgChain, avgChainT, avgChainT, avgChainT, avgChainT, avgChainT, avgChainT, avgChainTable, avgChainTable, avgChainTable, avgChainTable, avgChainTable, avgChainTable, avgHandles, avgNode, avgNodeArvRChain, avgNodeChain, avgNodeChainT, avgNodeChainT, avgNodeChainT, avgNodeChainT, avgNodeChainT, avgNodeChainT, avgNodeChainTable, avgNodeChainTable, avgNodeChainTable, avgNodeChainTable, avgNodeChainTable, avgNodeChainTable, avgNodeQLenChain, avgNodeResidTChain, avgNodeRespTChain, avgNodeT, avgNodeT, avgNodeT, avgNodeT, avgNodeT, avgNodeT, avgNodeTable, avgNodeTable, avgNodeTable, avgNodeTable, avgNodeTable, avgNodeTable, avgNodeTputChain, avgNodeUtilChain, avgQLen, avgQLenChain, avgQLenHandles, avgResidT, avgResidTChain, avgResidTHandles, avgRespT, avgRespTChain, avgRespTHandles, avgSys, avgSysRespT, avgSysT, avgSysT, avgSysT, avgSysTable, avgSysTable, avgSysTable, avgSysTput, avgT, avgT, avgT, avgT, avgT, avgT, avgTable, avgTable, avgTable, avgTable, avgTable, avgTable, avgTput, avgTputChain, avgTputHandles, avgUtil, avgUtilChain, avgUtilHandles, avgWaitT, bindingCapacityReason, cacheAvgT, cdfPassT, cdfPassT, cdfRespT, cdfRespT, chainAvgT, chainAvgT, chainAvgT, chainAvgT, chainAvgT, chainAvgT, citations, getAllSolvers, getAvg, getAvg, getAvg, getAvgArvR, getAvgArvRChain, getAvgArvRHandles, getAvgCacheT, getAvgCacheTable, getAvgChain, getAvgChainTable, getAvgChainTable, getAvgChainTable, getAvgChainTable, getAvgChainTable, getAvgChainTable, getAvgHandles, getAvgItemT, getAvgItemTable, getAvgLossT, getAvgLossTable, getAvgNode, getAvgNodeArvRChain, getAvgNodeChain, getAvgNodeChainTable, getAvgNodeChainTable, getAvgNodeChainTable, getAvgNodeChainTable, getAvgNodeChainTable, getAvgNodeChainTable, getAvgNodeQLenChain, getAvgNodeResidTChain, getAvgNodeRespTChain, getAvgNodeTable, getAvgNodeTable, getAvgNodeTable, getAvgNodeTable, getAvgNodeTable, getAvgNodeTable, getAvgNodeTputChain, getAvgNodeUtilChain, getAvgOrbit, getAvgOrbitT, getAvgOrbitTable, getAvgQLen, getAvgQLenChain, getAvgQLenHandles, getAvgRegionLossT, getAvgRegionLossTable, getAvgResidT, getAvgResidTChain, getAvgResidTHandles, getAvgRespT, getAvgRespTChain, getAvgRespTHandles, getAvgSys, getAvgSys, getAvgSys, getAvgSysRespT, getAvgSysTable, getAvgSysTable, getAvgSysTable, getAvgSysTput, getAvgT, getAvgT, getAvgT, getAvgT, getAvgT, getAvgT, getAvgTable, getAvgTable, getAvgTable, getAvgTable, getAvgTable, getAvgTable, getAvgTput, getAvgTputChain, getAvgTputHandles, getAvgUtil, getAvgUtilChain, getAvgUtilHandles, getAvgWaitT, getCdfPassT, getCdfPassT, getCdfRespT, getChainAvgT, getChainAvgT, getChainAvgT, getChainAvgT, getChainAvgT, getChainAvgT, getDeadlineTable, getLibrariesUsed, getModel, getMomentChainT, getMomentChainT, getMomentChainT, getMomentChainTable, getMomentChainTable, getMomentChainTable, getMomentStationT, getMomentStationT, getMomentStationT, getMomentStationTable, getMomentStationTable, getMomentStationTable, getMomentT, getMomentT, getMomentT, getMomentTable, getMomentTable, getMomentTable, getNodeAvgT, getNodeAvgT, getNodeAvgT, getNodeAvgT, getNodeAvgT, getNodeAvgT, getNodeChainAvgT, getNodeChainAvgT, getNodeChainAvgT, getNodeChainAvgT, getNodeChainAvgT, getNodeChainAvgT, getPerctRespT, getProb, getProb, getProbAggr, getProbAggr, getProbMarg, getSensitivityT, getSensitivityT, getSensitivityTable, getSensitivityTable, getStageT, getStageT, getStageTable, getStageTable, getSysAvgT, getSysAvgT, getSysAvgT, getTranAvg, getTranCdfPassT, getTranCdfPassT, getTranCdfRespT, getTranCdfRespT, getTranHandles, hasAvgResults, hasDistribResults, hasTranResults, initFromSolver, initHandles, itemAvgT, libraries, lossAvgT, mCT, mCT, mCT, model, momentChainT, momentChainT, momentChainT, momentStationT, momentStationT, momentStationT, momentT, momentT, momentT, mST, mST, mST, mT, mT, mT, nodeAvgT, nodeAvgT, nodeAvgT, nodeAvgT, nodeAvgT, nodeAvgT, nodeChainAvgT, nodeChainAvgT, nodeChainAvgT, nodeChainAvgT, nodeChainAvgT, nodeChainAvgT, options, orbitAvgT, print, prob, prob, probAggr, probAggr, probMarg, probMarg, probNormConstAggr, probSys, probSysAggr, regionLossAvgT, runAnalyzerChecks, sample, sampleAggr, sampleSys, sampleSysAggr, sensitivityT, sensitivityT, setAvgHandles, setAvgResults, setDistribResults, setLang, setModel, setTranAvgResults, setTranHandles, setTranProb, showLibraryAttribution, sT, sT, stageT, stageTable, sysAvgT, sysAvgT, sysAvgT, tranAvg, tranCdfPassT, tranCdfPassT, tranCdfRespT, tranCdfRespT, tranHandles

Methods inherited from class jline.solvers.Solver

getMethodFeatureSet, getName, getOptions, getResults, hasResults, isJavaAvailable, isStochastic, isValidOption, listValidOptions, parseOptions, parseOptions, reset, resetRandomGeneratorSeed, selectMethod, setChecks, setOptions, timeExceeded

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

SolverNC

public SolverNC(Network model,
 SolverOptions options)

SolverNC

public SolverNC(Network model)

SolverNC

public SolverNC(Network model,
 String method)

SolverNC

public SolverNC(Network model,
 Object... varargin)

Method Details

supportsExactSensitivity

public boolean supportsExactSensitivity()

The normalizing-constant solver is exact on the same product-form class that pfqn_sens differentiates, so getSensitivityTable uses the analytic branch.

Overrides:

supportsExactSensitivity in class NetworkSolver

Returns:

true

hasRetrievalCache

public static boolean hasRetrievalCache(NetworkStruct sn)

True if the model contains a Cache equipped with a delayed-hit retrieval system.

defaultOptions

public static SolverOptions defaultOptions()

getFeatureSet

public static FeatureSet getFeatureSet()

Returns the feature set supported by the NC solver

Returns:

- the feature set supported by the NC solver

getProb

public Double getProb(Node node,
 Matrix state)

getProbAggr

public Double getProbAggr(Node node,
 Matrix state_a)

Get aggregated probability for a specific node and state

Parameters:

node - The node to compute probability for

state_a - The aggregated state (optional, uses current state if null)

Returns:

The aggregated probability value

getProbAggr

public Double getProbAggr(Node node)

Get aggregated probability for a specific node using current state

Parameters:

node - The node to compute probability for

Returns:

The aggregated probability value

getProbMarg

public Ret.ProbabilityResult getProbMarg(int ist,
 int jobclass,
 Matrix state_m)

Marginal queue-length distribution at a station, routing the CLI's (station, class) call to the exact procomom/enumeration path in getProbMarg(Node). The class index is not used: NC returns the per-station total-jobs marginal.

Overrides:

getProbMarg in class NetworkSolver

Parameters:

ist - station index

jobclass - class index (unused; marginal is over total jobs)

state_m - optional state (unused)

Returns:

the exact marginal probability distribution

getProbMarg

public Ret.ProbabilityResult getProbMarg(Node node)

getProbNormConstAggr

public Ret.ProbabilityResult getProbNormConstAggr()

Get the log normalization constant for aggregated probabilities

Overrides:

getProbNormConstAggr in class NetworkSolver

Returns:

The log normalization constant

Throws:

IllegalAccessException - if analysis fails

getProbSys

public Ret.ProbabilityResult getProbSys()

Get system-wide joint probability

Overrides:

getProbSys in class NetworkSolver

Returns:

The joint probability value

getProbSysAggr

public Ret.ProbabilityResult getProbSysAggr()

Get aggregated system-wide joint probability

Overrides:

getProbSysAggr in class NetworkSolver

Returns:

The aggregated joint probability value

getStruct

public NetworkStruct getStruct()

setStruct

public void setStruct(NetworkStruct sn)

runAnalyzer

public void runAnalyzer()
                 throws IllegalAccessException

Description copied from class: Solver

Executes the solver algorithm to analyze the model. This abstract method must be implemented by concrete solver classes.

Specified by:

runAnalyzer in class Solver

Throws:

IllegalAccessException - if access to required resources is denied

getNormalizingConstant

public SolverNC.NormalizingConstantResult getNormalizingConstant()
                                                          throws IllegalAccessException

Get the normalizing constant and its logarithm

Returns:

NormalizingConstantResult containing the normalizing constant and its logarithm

Throws:

IllegalAccessException - if analysis fails

listValidMethods

public String[] listValidMethods()

List all valid solution methods for this solver

Returns:

array of valid method names

meOpen

public NCResult meOpen()

Maximum Entropy algorithm for Open Queueing Networks. Applies the ME algorithm from Kouvatsos (1994) to the model. Only supports open queueing networks (no closed classes).

Returns:

NCResult containing ME algorithm results

memUnsupportedReason

public static String memUnsupportedReason(NetworkStruct sn)

Returns null when the Maximum Entropy Method (Kouvatsos 1994) supports the features of the model described by sn (node types, class switching, scheduling, source presence); otherwise a message describing the first unsupported feature. Open/closed class membership is checked separately on the model; this overload assumes the open-network variant.

memUnsupportedReason

public static String memUnsupportedReason(NetworkStruct sn,
 boolean closed)

Returns null when the Maximum Entropy Method (Kouvatsos 1994) supports the features of the model described by sn; otherwise a message describing the first unsupported feature. Open models (Section 3.2) allow Source, Queue, Delay and Sink nodes with GE/GE/1, GE/GE/c and GE/GE/inf building blocks; closed models (Section 3.3) allow Queue and Delay nodes with G/G/1 and G/G/inf building blocks only, so finite multiserver stations are rejected.

memUnsupportedReason

public static String memUnsupportedReason(NetworkStruct sn,
 boolean hasOpen,
 boolean hasClosed)

Feature check for the Maximum Entropy Method given the class composition of the model: hasOpen/hasClosed flag the presence of open and closed classes (both true for mixed models). Returns null when supported, otherwise the first unsupported feature found.

memBufferSize

public static double memBufferSize(NetworkStruct sn,
 int ist)

Physical buffer size of a station in jobs, in service included: the tighter of the station capacity and the sum of the per-class capacities, infinite when the station is unbounded.

Only a buffer that can actually BIND is reported. refreshCapacity derives a FINITE classcap (the chain population) for EVERY closed model, so a plain finiteness test would report a buffer at every station of every closed model; a capacity at least as large as the total population can never refuse a job and is returned as infinite. The population sum is infinite as soon as one class is open, so any finite capacity reachable by an open class binds.

Parameters:

sn - network structure

ist - station index

Returns:

buffer size in jobs

memDropRule

public static DropStrategy memDropRule(NetworkStruct sn,
 int ist)

Drop rule declared at a station for the first class, defaulting to Drop, which is what refreshCapacity assigns to a finite buffer reachable by an open class.

Parameters:

sn - network structure

ist - station index

Returns:

the drop strategy in force at the station

memHasFiniteBuffers

public static boolean memHasFiniteBuffers(NetworkStruct sn)

True when the model is a single-class open network carrying a finite buffer that MEM represents explicitly, as a censored GE/GE/c/0;N queue. Callers use it to bypass the product-form capacity gate, which exists because the other NC methods have no representation of a buffer.

Parameters:

sn - network structure

Returns:

true when the finite-buffer MEM path applies

meClosed

public NCResult meClosed()

Maximum Entropy algorithm for Closed Queueing Networks (Kouvatsos 1994, Section 3.3): pseudo-open decomposition followed by a convolution over the population lattice, iterated on the flow (work rate) equations.

Returns:

NCResult containing the closed ME algorithm results

meClosed

public NCResult meClosed(MeOqnOptions meOptions)

Maximum Entropy algorithm for Closed Queueing Networks with custom options (Kouvatsos 1994, Section 3.3).

Parameters:

meOptions - options for the ME algorithm

Returns:

NCResult containing the closed ME algorithm results

meMixed

public NCResult meMixed()

Maximum Entropy algorithm for Mixed Queueing Networks: composition of the open (Section 3.2) and closed (Section 3.3) algorithms with product-form-style conditioning.

Returns:

NCResult containing the mixed ME algorithm results

meMixed

public NCResult meMixed(MeOqnOptions meOptions)

Maximum Entropy algorithm for Mixed Queueing Networks with custom options.

Parameters:

meOptions - options for the ME algorithm

Returns:

NCResult containing the mixed ME algorithm results

meOpen

public NCResult meOpen(MeOqnOptions meOptions)

Maximum Entropy algorithm for Open Queueing Networks with custom options. Applies the ME algorithm from Kouvatsos (1994) to the model. Only supports open queueing networks (no closed classes).

Parameters:

meOptions - Options for the ME algorithm

Returns:

NCResult containing ME algorithm results

sample

public Object sample(Node node,
 int numEvents)

Sample node state trajectory

Parameters:

node - The node to sample

numEvents - Number of samples to generate

Returns:

Sample result containing state trajectory

sample

public Object sample(Node node)

Sample node state trajectory with default number of samples

Parameters:

node - The node to sample

Returns:

Sample result containing state trajectory

supports

public boolean supports(Network model)

Checks whether the given model is supported by the NC solver

Overrides:

supports in class Solver

Parameters:

model - - the network model

Returns:

- true if the model is supported, false otherwise

memFiniteBufferPath

public static boolean memFiniteBufferPath(NetworkStruct sn,
 SolverOptions options)

True when method='mem' has been requested on a model whose finite buffers MEM carries explicitly. Callers use it to skip the product-form capacity gate, which exists only because the other NC methods cannot represent a buffer.

Parameters:

sn - network structure

options - solver options, whose method selects MEM

Returns:

true when the finite-buffer MEM path applies

resolveMethod

public String resolveMethod(SolverOptions options)

Feature-driven resolution of method='default': an open network with non-Markovian (non-unit SCV) variability within the MEM feature set is solved by the Maximum Entropy Method by default, since the normalizing-constant path would silently exponentialize it. Mirrors the dispatch in runAnalyzer and the MATLAB/Python SolverNC.resolveMethod.

Overrides:

resolveMethod in class Solver

Parameters:

options - the solver options

Returns:

the concrete method name

supportsModelMethod

public String supportsModelMethod(String method)

Method-aware gate. MEM (Kouvatsos maximum entropy) has structural applicability rules beyond a flat feature set (open-only, Source/Queue/ Delay/Sink, non-priority scheduling); delegate to memUnsupportedReason, which returns a precise reason (null when supported). All other NC methods use the coarse product-form feature gate.

Overrides:

supportsModelMethod in class Solver

Parameters:

method - the concrete method name

Returns:

empty string if supported, else the offending reason

isStochasticMethod

public boolean isStochasticMethod(String method)

NC is deterministic except for the Monte Carlo integration methods (mci/imci), logistic sampling (ls), and the sampling method, whose estimates depend on the random seed. Method names are tokenized so that runtime-resolved names such as "default/imci" and prefixed names such as "nc.ls" classify correctly.

Overrides:

isStochasticMethod in class Solver

Parameters:

method - the method name to classify

Returns:

true if the method returns stochastic estimates

supportsModel

public static boolean supportsModel(Network model)

Static method to check whether the given model is supported by the NC solver. This allows checking support without creating a solver instance.

Parameters:

model - - the network model

Returns:

- true if the model is supported, false otherwise

getCdfRespT

public Matrix getCdfRespT(AvgHandle... R)

Get cumulative distribution function of response times at FCFS and delay nodes

Parameters:

R - Optional response time handles (currently unused in this implementation)

Returns:

Response time distribution matrix for each node and class

getCdfRespT

public Ret.DistributionResult getCdfRespT()

Get cumulative distribution function of response times with default parameters

Overrides:

getCdfRespT in class NetworkSolver

Returns:

Response time distribution matrix for each node and class