Package jline.api.lqn

Class Lqn_mol

java.lang.Object

jline.api.lqn.Lqn_mol

public final class Lqn_mol
extends Object

Method of Layers on the SRVN decomposition of a layered queueing network whose entries carry no activity graph.

A compact, self-contained reimplementation of the layered fixed point SolverLN runs, restricted to LQNs in which every entry binds exactly one activity and there are no activity precedences. It decomposes the model the way lqns --srvn-layering does -- one submodel per processor and one per called task -- and sweeps them in the two phases of Rolia-Sevcik's Method of Layers: all software (task) submodels, then all hardware (processor) ones.

Every submodel is a closed multiclass queueing network with ONE station and one class per client task, so it is solved by Pfqn_qdamva rather than by building a Network. The surrogate client delay of SolverLN collapses into the think-time vector Z of that call.

WHERE THIS DIFFERS FROM SolverLN's srvn.cs: a submodel here carries one class per client TASK with visit-weighted demands, where srvn.cs carries one class per activity and encodes the call multiplicities as routing. On an entry-only model the two agree on the structure and differ only in the aggregation, so the throughputs and processor utilizations track closely while entry response times spread more.

SCOPE. Entry-only models. Activity graphs (fork/join, OR-branches, loops, second phases, forwarding), asynchronous calls, caches, setup tasks, admission constraints, replication and open arrivals are REFUSED, not approximated, and named when they are.

INDEX BASE. LayeredNetworkStruct is 0-BASED, unlike MATLAB's lsn, which numbers from 1: hosts run 0..nhosts-1, tasks tshift..tshift+ntasks-1, and so on, with nidx the element COUNT. Every loop here is written in that convention.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	Lqn_mol.Options	Tuning of the outer fixed point.
static class	Lqn_mol.Result	The four (nidx) vectors in the column convention SolverLN and LQNS report, so they line up with LN(model).getAvgTable() cell for cell.

Method Summary

Modifier and Type	Method	Description
static Lqn_mol.Result	lqn_mol(LayeredNetworkStruct lsn)	Method of Layers with the reference's default tuning.
static Lqn_mol.Result	lqn_mol(LayeredNetworkStruct lsn, Lqn_mol.Options options)	Method of Layers on an entry-only, closed, synchronous LQN.

Methods inherited from class java.lang.Object

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

Method Details

lqn_mol

public static Lqn_mol.Result lqn_mol(LayeredNetworkStruct lsn)

Method of Layers with the reference's default tuning.

lqn_mol

public static Lqn_mol.Result lqn_mol(LayeredNetworkStruct lsn,
 Lqn_mol.Options options)

Method of Layers on an entry-only, closed, synchronous LQN.

Parameters:

lsn - the layered struct, from LayeredNetwork.getStruct()

options - iteration cap, tolerance and under-relaxation factor

Returns:

the four measure vectors and the fixed-point state behind them