Package jline.solvers.nc.analyzers

Class Solver_nc_lossn_analyzer

java.lang.Object

jline.solvers.nc.analyzers.Solver_nc_lossn_analyzer

public final class Solver_nc_lossn_analyzer
extends Object

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final class	Solver_nc_lossn_analyzer.LossnInfo	Inputs of the loss network solved by the analyzer, kept on the result so that SolverNC.getAvgLinkTable can report the per-link measures with the same method without rebuilding the constraints.

Method Summary

Modifier and Type	Method	Description
static NCResult	solver_nc_lossn_analyzer(NetworkStruct sn, SolverOptions options)	Analyzes open loss networks with FCR.

Methods inherited from class java.lang.Object

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

Method Details

solver_nc_lossn_analyzer

public static NCResult solver_nc_lossn_analyzer(NetworkStruct sn,
 SolverOptions options)

Analyzes open loss networks with FCR. Handles open queueing networks with a single multiclass Delay node inside a Finite Capacity Region (FCR) with DROP policy. The FCR admission rule is A n <= C on the per-class occupancy vector n of the region, where the rows of A are assembled from every constraint the region declares: the global job cap, the memory budget weighted by the per-class sizes, the per-class job caps, and any explicit linear constraint set with FiniteCapacityRegion.setConstraint. Rows left unbounded are DROPPED rather than given a surrogate capacity. Method selection (options.method): "rec" - MDD-rec (Lossn_rec): the same constant as the exact sum over the admissible set, obtained by one memoised walk of the decision diagram holding it. Places no integrality demand on A or C. "exact" (default) - Manjunath-Sikdar transform (Lossn_manjunath): the normalization constant is obtained exactly as a multidimensional contour integral evaluated by residues. Requires integer A and C. "erlangfp" - Erlang fixed-point (reduced-load) approximation. "asy" - Large-capacity (Kelly) limit (Lossn_asy): link blocking from a convex dual problem, no state summation; accurate in heavy traffic and for large capacities, zero loss on underloaded links. "mci" - Monte Carlo importance-sampling summation (Ross-Wang 1992): estimates the normalization constant g(C) and class blocking with confidence intervals (options.samples/options.seed). The default is the residue transform on an integral region and MDD-rec on a fractional one. The residue argument counts whole units; MDD-rec needs only that the admissible set be finite and bounded per coordinate, which it still is, so the fractional case is exact as well.