Package jline.api.lossn

package jline.api.lossn

APIs for stochastic models of loss networks and blocking systems.

This package contains algorithms and methods for analyzing queueing systems where jobs can be blocked or lost when resources are unavailable. It supports analysis of finite-capacity systems and blocking probabilities.

Since:

LINE 2.0

Related Packages

Package	Description
jline.api	Procedural algorithms for solving stochastic models and analyzing queueing systems.

Classes

Class	Description
Lossn_asy	Computes the fluid limit of a loss network by convex duality.
Lossn_asy.LossnAsyResult	Result of Lossn_asy.lossn_asy(Matrix, Matrix, Matrix).
Lossn_erlangfp
Lossn_kr	Kaufman-Roberts recursion for the multirate single-link loss system (stochastic knapsack).
Lossn_kr_asy	Normal approximation of multirate single-link blocking for large C.
Lossn_kr_asy.LossnKrAsyResult	Result of Lossn_kr_asy.lossn_kr_asy(Matrix, Matrix, double).
Lossn_kr_dim	Dimensions a multirate link by the Kaufman-Roberts recursion.
Lossn_kr_dim.LossnKrDimResult	Result of Lossn_kr_dim.lossn_kr_dim(Matrix, Matrix, Matrix).
Lossn_kr.LossnKrResult	Result of Lossn_kr.lossn_kr(Matrix, Matrix, double).
Lossn_links	Link-level performance measures of a product-form loss network.
Lossn_links.LossnLinksResult	Result of Lossn_links.lossn_links(Matrix, Matrix, Matrix, String, Lossn_mci.Options).
Lossn_manjunath	Exact normalization constant, carried load and blocking of a loss network by the transform technique of Manjunath and Sikdar.
Lossn_mci	Monte Carlo importance-sampling summation for loss networks, after Ross and Wang, "Monte Carlo Summation Applied to Product-Form Loss Networks", Probability in the Engineering and Informational Sciences, 6 (1992), 323-348.
Lossn_mci.Options	Options of Lossn_mci.lossn_mci(Matrix, Matrix, Matrix, Options), mirroring the fields of the MATLAB options struct.
Lossn_rec	Exact analysis of a loss network by MDD-rec: the normalising constant is the sum of a product form over the admissible set {n >= 0 : A n <= C}, which is what a decision diagram holding that set computes in one memoised walk.
Lossn_rec.LossnRecResult	Carried load, blocking, log normalising constant and walk count.
Lossn_revsens	Derivative of the long-run revenue rate of a loss network with respect to the offered loads, and the implied costs.
Lossn_revsens.LossnRevsensResult	Result of Lossn_revsens.lossn_revsens(Matrix, Matrix, Matrix, Matrix, String, Lossn_mci.Options).
Lossn_revsens.MciCI	Monte Carlo confidence information returned by the 'mci' method.