MarkovClassifier

simple module

This module implements a packet classifier using a Markov process that has as many states as output gates the classifier has. The output gate for a given packet is determined by the current state of the Markov process.

Inheritance diagram

The following diagram shows inheritance relationships for this type. Unresolved types are missing from the diagram.

Extends

Name	Type	Description
PacketClassifierBase	simple module	This is a base module for various packet classifier modules. Derived modules must implement a single packet classifier function which determines the index of the output gate for the next pushed packet.

Parameters

Name	Type	Default value	Description
displayStringTextFormat	string	"classified %p pk (%l)\ncurrent state: %s"	determines the text that is written on top of the submodule
initialState	int	0	the index of the start state of the Markov process
transitionProbabilities	string		the transition matrix (N x N) of the Markov process, specified as a list of probabilities
waitIntervals	string		the amount of time the Markov process stays in a given state, a list of intervals (N) indexed by the state

Properties

Name	Value	Description
display	i=block/classifier
class	MarkovClassifier

Gates

Name	Direction	Size	Description
in	input
out [ ]	output

Signals

Name	Type	Unit
packetPushed	inet::Packet

Statistics

Name	Title	Source	Record	Unit	Interpolation Mode
packetPushed	packets pushed		count, sum(packetBytes), vector(packetBytes)		none

Source code

//
// This module implements a packet classifier using a Markov process that has
// as many states as output gates the classifier has. The output gate for
// a given packet is determined by the current state of the Markov process.
//
simple MarkovClassifier extends PacketClassifierBase like IPacketClassifier
{
    parameters:
        displayStringTextFormat = default("classified %p pk (%l)\ncurrent state: %s");
        int initialState = default(0); // the index of the start state of the Markov process
        string transitionProbabilities; // the transition matrix (N x N) of the Markov process, specified as a list of probabilities
        string waitIntervals; // the amount of time the Markov process stays in a given state, a list of intervals (N) indexed by the state 
        @class(MarkovClassifier);
}

File: src/inet/queueing/classifier/MarkovClassifier.ned