Model Environment Module

The following files are generated by the Model Environment Module:

$\langle name\_of\_the\_model\rangle.obj$ - This file is generated by the TGIF tool. This is a ASCII text file, that has a graphic representation of the model and is composed by the TGIF functions.
$\langle name\_of\_the\_model\rangle.parser$ - This file is a C program text. All objects in the model are described, with its attributes, events and so on. This file is the output for the grammar.c program.
$\langle name\_of\_the\_model\rangle.events$ - This file lists all events in the model. The format is:
```
       name of object.name of event
```
$\langle name\_of\_the\_model\rangle.states$ - The entire state space is described here. The format is:
```
state number   all state variables of the model
```
The state variables are listed in the same order as in the $<name\_of\_the\_model>.<vstat>$ file.
$\langle name\_of\_the\_model\rangle.vstat$ - This file has all state variables. The format is:
```
 name of object.state_variable
```
$\langle name\_of\_the\_model\rangle.state\_variable$
$\langle name\_of\_the\_model\rangle.maxvalues$ - This file has the state variables with their respectives maximum values (these values are relationed with the specified model). The format is:
```
 name of object.state   maximum value
```
$\langle name\_of\_the\_model\rangle.user.code.c$ - This file is generated automatically. It contains the user code for actions, messages and expressions.
$\langle name\_of\_the\_model\rangle.parameter$ - This file is always generated. But it is fill in only if the model has the literal parameter. There is a association between a parameter and a letter.The format is:
```
letter  name of object.parameter
```
$\langle name\_of\_the\_model\rangle.generator\_mtx$ - This file represents the generator matrix of the model. The format is:
```
 previous state   actual state   transition probability
```
$\langle name\_of\_the\_model\rangle.generator\_mtx\_expr$ - This file is always generated. But it is fill in only if the model has the literal parameter.The format is:
```
total number of the parameters
letter name of object.parameter (association)
number letter (association)
```
$\langle name\_of\_the\_model\rangle.generator\_mtx\_param$ - This file represents the generator matrix of the model, but intead of the absolute value, the transition probabilities are the parameters specified in the model.
$\langle name\_of\_the\_model\rangle.NM.st\_trans\_prob\_mtx$ - This file represents the transition probabilities matrix and it is generated only for Non Markovian Models. The format is:
```
previous state   actual state   probability
```
$\langle name\_of\_the\_model\rangle.tables\_dump$
$\langle name\_of\_the\_model\rangle.rate\_reward.\langle name\_of\_object.name\_of\_the\_reward\rangle$ - This file is generated by the Mathematical Model Module and has the rate reward in each state, when the condition of the reward is true. The format is:
```
state  value of reward
```
$\langle name\_of\_the\_model\rangle.impulse\_reward.\langle name\_of\_object.name\_of\_the\_reward\rangle$ - This file is generated by the Mathematical Model Module and has the impulse reward.
$\langle name\_of\_the\_model\rangle.rate\_reward.GlobalReward.\langle.name\_of\_the\_reward\rangle$ - This file is generated by the Mathematical Model Module and has the global rate reward.
$\langle name\_of\_the\_model\rangle.rate\_reward.expr$ - This file has the expression of the reward.
$\langle name\_of\_the\_model\rangle.reward\_levels.\langle name\_of\_the\_reward\rangle$
$\langle name\_of\_the\_model\rangle.absorb\_st$ - This file has all absorbing states in the model.
$\langle name\_of\_the\_model\rangle.config$ - This file is generated by the TANGRAM-II interface (menu $\rightarrow$ config). These settings are used to set some parameters in TANGRAM-II tool.
$\langle name\_of\_the\_model\rangle.OUT.\langle name\_of\_the\_output\_file\rangle$
$\langle name\_of\_the\_model\rangle.uniform\_rate$ - This file has the uniformization rate of the Continuos Markov Chain. The format is:
```
uniformization_rate
```
The files below are generated only for non-Markovian models:
1. $\langle name\_of\_the\_model\rangle.NM.st\_trans\_prob\_mtx$ : This file is generated only for Non Markovian Models. It includes the transition probabilities of the uniformized Markov chain that is obtained when all the events in the model are assumed to have exponential rates. The format is:
```
previous state  actual state  probability
```
2. $\langle name\_of\_the\_model\rangle.NM.chns\_betw\_embed\_pnts$ : This file contains, for each deterministic event, the Markov chains that are obtained between embedded points (details in the solution technique used). The following information is included:
  1. The uniformization rate (taken from the exponential matrix considering all events exponential).
  2. event; id of the deterministic event; number of independent chains; boolean variable (1): there is an absorbing state; (0) otherwise; inverse of the deterministic rate.
  3. -1; id of the state variable; id of the independent chain; -1 0 0: delimiter
  4. Definition of each independent chain for the event: chain; id of the independent chain; number of states; uniformization rate for the independent chain; state state probability (the state id is already properly renumbered); -1 0 0
3. $\langle name\_of\_the\_model\rangle.NM.embedded\_points$ : This file contains all the transitions (from state, to state) that are associated with the firing of a deterministic event, i.e., all the embedded points considering only the execution of a deterministic event.
4. $\langle name\_of\_the\_model\rangle.NM.embedded\_points\_expr$
5. $\langle name\_of\_the\_model\rangle.NM.states\_det\_ev$ : This file contains all the states in which a deterministic event is enabled.
6. $\langle name\_of\_the\_model\rangle.NM.embedded\_chain\_mapping$ : This is the mapping from the state id of the Markov chain considering all events with exponential rates to the state id of the resulting embedded chain.
7. $\langle name\_of\_the\_model\rangle.NM.embedded\_chain$
8. $\langle name\_of\_the\_model\rangle.NM.emb\_points\_st\_probs$ : The steady-state probabilities for the embedded chain.
9. $\langle name\_of\_the\_model\rangle.NM.expected\_cycle\_length$ : The expected length of the intervals between embedded points.
10. $\langle name\_of\_the\_model\rangle.NM.interest\_measures$ : Indicates which state variables are considered for the calculation of the marginal probabilities. Note: although not included in the interface, joint probabilities can be calculated as well using this file.
11. $\langle name\_of\_the\_model\rangle.NM.marginal\_probs$ : The resulting marginal probabilities obtained for the non-Markovian models. The state variables used for calculating the marginal probabilities are specified by the user in the interface.
$\langle name\_of\_the\_model\rangle.partition$ - This file lists all partitions used in the GTH block method.
$\langle name\_of\_the\_model\rangle.SS.gth$ - This file is generated by the Analytical Solution Module - Stationary State GTH Solution Method, and has the vector probabilities in steady state. The format is:
```
state   probability
```
$\langle name\_of\_the\_model\rangle.SS.gthb$ - This file is generated by the Analytical Solution Module - Stationary State GTH block version Solution Method, and has the vector probabilities in steady state. The format is
```
state  probability
```
$\langle name\_of\_the\_model\rangle.SS.jacobi$ - This file is generated by the Analytical Solution Module - Stationary State Jacobi Solution Method, and has the vector probabilities in steady state. The format is:
```
number of iterations 
state  probability
```
$\langle name\_of\_the\_model\rangle.SS.gauss$ - This file is generated by the Analytical Solution Module - Stationary State Gauss-Seidel Solution Method, and has the vector probabilities in steady state. The format is:
```
number of iterations 
state  probability
```
$\langle name\_of\_the\_model\rangle.SS.power$ - This file is generated by the Analytical Solution Module - Stationary State Power Solution Method, and has the vector probabilities in steady state. The format is:
```
number of iterations
state  probability
```
$\langle name\_of\_the\_model\rangle.SS.sor$ - This file is generated by the Analytical Solution Module - Stationary State Succesive Over Relaxation Solution Method - and has the vector probabilities in steady state. The format is:
```
number of iterations
state  probability
```
$\langle name\_of\_the\_model\rangle.TS.pp.\langle TIME\rangle$
$\langle name\_of\_the\_model\rangle.TS.brew.cumulat\_distrib$
$\langle name\_of\_the\_model\rangle.TS.brew.expected\_period$
$\langle name\_of\_the\_model\rangle.TS.exptr$
$\langle name\_of\_the\_model\rangle.TS.operational\_time$
$\langle name\_of\_the\_model\rangle.SIMUL.\langle name\_of\_the\_simulation\_result\rangle$ - This file is generated by the Simulation Solution Module - Batch Simulation and Rare Simulation - and has all informations about the rewards specified in the model (Rewards part).
$\langle name\_of\_the\_model\rangle.threshold$ - This file is generated by the Simulation Solution Module - Rare Simulation. The format is:
```
<name of object>.<state variable used in rare simulation>
threshold  splits
```
$\langle name\_of\_the\_model\rangle.INTSIMUL.\langle name\_of\_the\_simulation\_result\rangle$

$\langle name\_of\_the\_model\rangle.IM.\langle name\_of\_measure\_of\_interest\rangle$ - This file is generated by the Measures of Interest Module. The format depends of the kind of the measure calculated:

PMF of one or more state variables
Without Conditional:

name of the measure
expected value of the measure
probability mass function (PMF) of the state variable 
choosen

With Conditional:

name of the measure
expected value of the measure 
probability of the condition is true in the model
list of all states when the Conditional is true

Function of state variables
Without Conditional:

name of the measure
function
expected value of the function
0.0 - probability of the function is false in the model
1.0 - probability of the function is true in the model

With Conditional:

name of the measure
function
condition
probability of the condition is true in the model
condition expected value of the function
0.0 - probability of the conditional function is false in 
the  model
1.0 - probability of the condtional function is  true  in 
the model

Probability of a set
Without Conditional:

name of the measure
set description
set probability
1 - set probability

With Conditional

name of the measure
set description
conditional
conditional set probability

The following files are generated by the Traffic Modeling Module, in the Model Environment Module:

Markovian Models:
1. $\langle name\_of\_the\_model\rangle.intervals$ - This file is generated by the interface. The format is:
```
number of intervals 
initial observation time   final observation time   number 
of points
```
2. $\langle name\_of\_the\_model\rangle.init\_prob$ - This file is generated by the interface and has the initial probability. The format is:
```
initial state   probability
```
3. $\langle name\_of\_the\_model\rangle.idc$ - This file has the idc measure. The format is:
```
observation time  IDC  mean E[N(t)]  variance Var[N(t)] 
second moment E[N2(t)]
```
4. $\langle name\_of\_the\_model\rangle.autocovariance$ - This file has the autocovariance measure. The format is:
```
observation time    Cov[X(t),X(t+time)]
```
5. $\langle name\_of\_the\_model\rangle.autocorrelation$ - This file has the autocorrelation measure. The format is:
```
observation time    Cor[X(t),X(t+time)]
```
6. $\langle name\_of\_the\_model\rangle.stationary\_descriptors$ - This file has the stationary descriptors measures: mean, second moment, variance, burtiness and peak value. The format is:
```
expected value
second moment
variance value
peak value
burstiness
```
Traces:
1. $\langle name\_of\_the\_model\rangle.seq\_idc$ - This file has the idc measure. The format is:
```
observation time  IDC  mean E[N(t)]  variance Var[N(t)] 
 second moment E[N2(t)]
```
2. $\langle name\_of\_the\_model\rangle.seq\_autocovariance$ - This file has the autocovariance measure. The format is:
```
observation time    Cov[X(t),X(t+time)]
```
3. $\langle name\_of\_the\_model\rangle.seq\_autocorrelation$ - This file has the autocorrelation measure. The format is:
```
observation time    Cor[X(t),X(t+time)]
```
4. $\langle name\_of\_the\_model\rangle.stationary\_descriptors$ - This file has the stationary descriptors measures. The format is:
```
min rate  
max rate
expected value   E[trace]
variance value   E[trace]
```

Guilherme Dutra Gonzaga Jaime 2010-10-27