möbius tutorial - fmtfmt.cs.utwente.nl/tools/motor/misc_doc/mobiustutorial.pdf · möbius tutorial...

Möbius Tutorial

Data Networking LectureSummer Term 2006May 3, 2006

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Dependable Systems and SoftwareUniversität des Saarlandes

Outline

Möbius, MoTor & InstallationCreating Projects and MoDeST ModelsDefining Reward VariablesCreating StudiesSimulating the Model

Möbius, MoTor & Installation

MöbiusSoftware for modeling behaviors of complex systemsSupport multiple modeling formalism and solution techniquesDeveloped in PERFORM Group at the UIUChttp://www.mobius.uiuc.edu

MoTor (MOdest TOol EnviRonment)Modeling and analysis of MoDeST specificationsAs a backend in MobiusDeveloped in FMT Group at University of Twentehttp://fmt.cs.utwente.nl/tools/motor/


MöbiusSoftware for modeling behaviors of complex systemsSupport multiple modeling formalism and solution techniquesDeveloped in PERFORM Group at the UIUChttp://www.mobius.uiuc.edu

MoTor (MoDeST Tool Environment)Modeling and analysis of MoDeST specificationsAs a backend in MöbiusDeveloped in FMT Group at University of Twentehttp://fmt.cs.utwente.nl/tools/motor/


Using Möbius & Motor in CIP computersLog in to one of computers in CIP roomsSet some environment variablesConsult the installation guide for details (available in the CMS)

Installing on your computerObtain the source in our groupConsult the installation guide for pre-requisitesPerform the installation described in the guide


Using Möbius & Motor in CIP computersLog in to one of computers in CIP roomsSet some environment variablesConsult the installation guide for details (available in the CMS)

Installing on your computerObtain the source from our groupConsult the installation guide for pre-requisitesPerform the installation as described in the guide

Outline


Creating Projects and MoDeST ModelsLadies and Gent’s, Meet Möbius!

1

2Möbius system notificationdescribes the progress and results of compilations, opened projects, etc.

Creating Projects and MoDeST ModelsThe First Möbius Project

1Menu Project New

2 Provide a project name

3

Creating Projects and MoDeST ModelsThe First Möbius Project

1The Project Editor

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The tree-view of the model and solution types in Möbius framework

Creating Projects and MoDeST ModelsThe First Atomic Project (in this case a MoDeST model)

Creating a new atomic model. Right-click “Atomic”and select “New”.

Creating Projects and MoDeST ModelsMoDeST Model

1 Provide a name forthe model

2

Several atomic model types. We are creating a model with MoDeST formalism.

4NOTE: since we are only going to use MoDeST, we will not describe the others. For those who are curious, consult the Möbius Manual available in it website.

3

Creating Projects and MoDeST ModelsMoDeST Model: M|D|1|n

1 The MoDeST Editor

Edit your model here.For the time being, wejust use the M|D|1|nmodel described inlast lectures.

23External Constants

Global VariablesRemember themfor later

Creating Projects and MoDeST ModelsThe Compilation of MoDeST Model

2

Menu File Save1

Saving it is compiling it!

3NOTE: the compilation disables the menu of the MoDeST Editor. If it compiles successfully, the menu is enabled back. It takes some time to compile.

Creating Projects and MoDeST ModelsWhat If Something Goes Wrong?

1 This window informs you that something’s wrong.

2 What’s wrong is described here. Fix it and re-save again.

Outline


Defining Reward VariablesDefining What We Want to Measure

Creating a new reward model. Right-click “Reward”and select “New”.

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2NOTE: Reward formalism define functions that measure information about the system being modeled.

Defining Reward VariablesNaming the Reward Model

1 Provide a name forthe reward model

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Defining Reward VariablesMeasuring upon Which?

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3 Performance Variable Editor

Select the atomic model upon which the reward model is defined.

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Corresponds to the tab “Model”

Defining Reward VariablesA Brand-New Performance Variable

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Define a new performance variable.

NOTE: a performance variable is a function of MoDeST model’s global variables [Rate Rewards], or of the occurrences of actions in the MoDeST model [Impulse Rewards].

Defining Reward VariablesPerformance Variables’ List

1Created Variables.

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Ways to manipulate them.

Defining Reward VariablesReward Functions

1 MoDeST Model’s global variables. Remember?

2 The reward function definition of PV “Overflows”. In this case, just obtaining the value of MoDeSTmodel’s global variable “Overflows”.

Defining Reward VariablesThe Timing

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Time defines when thereward function is evaluated.

There are 4 types of “Time”.How many evaluations? Provide the number of entries below.

NOTE: Time = “Instant of Time”. The function is evaluated exactly at time “Start Time”.

Defining Reward VariablesInterval of Time

1NOTE: Time = “Interval of Time”. Returns all possible values of the reward function weighted by the amount of time each value is in existence from “Start Time”to “End Time”.

Defining Reward VariablesTime Averaged Interval

1NOTE: Time = “Time Averaged Interval”. Returns the interval of time result, divided by the length of time for the interval. The “Start Time” and “End Time” are the same as previous.

Defining Reward VariablesSteady State

1NOTE: Time = “Steady State”. The reward function is evaluated after the system being modeled reaches steady-state. Assuming that it takes “Initial Transient”time to reach it, and then data is gathered multiple times according to “Batch Size”.

Defining Reward VariablesSimulating the Performance Variables

How do we like the performance variable to be estimated. Details is described in lecture or can be obtained in the Möbiusmanual available in the tool’s website.

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The required confidence of the result. With probability 0.95 the estimation value lies in the confidence interval.Relative interval depends on the estimation value while the Absolute interval is absolute.

Defining Reward VariablesAnother Performance Variable

1 Define another PV “NumberOfPackets”as done before

2The reward function definition. Just obtaining the value of the global variable “NumberOfPackets”.

Defining Reward VariablesDon’t Forget to Save

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Save the reward model. Remember, saving it is compiling it. It may takes some time.

Outline


Creating StudiesList of Experiments

Creating a new study. Right-click “Study” and select “New”.

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2NOTE: A Study defines a set of experiments.

Creating StudiesNaming the Studies

1 Provide a name forthe Study

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Choose the type of the Study.

Creating StudiesThe Set Study

1Select the reward model upon which the study is defined.

23 Set Study Editor

Creating StudiesExperiments

1The MoDeST model’s external constants. 2

For each experiment, set the values of the external constants.

Creating StudiesCreating New Experiments

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Click “Add” to create a new experiment.

2Set the values of the external constants for the new experiment.

Creating StudiesDon’t Forget to Save

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Save the study. Remember, saving it is compiling it. It may takes some time.

Creating StudiesThe Range Study

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Range Study Editor

2 3

4Save it!

Incremental: additive, multiplicative, exponential.

Based on incremental function.

The way an external constant takes/varies it values.

Outline


Simulating the ModelA New Solver: a Simulation

Creating a new solver. Right-click “Solver” and select “New”.

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2NOTE: A Solver defines a way to solve a Study (a setof experiments).

Simulating the ModelMöbius Simulator

1 Provide a name forthe Solver

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Choose the type of the Solver Möbius Simulator.

Simulating the ModelWhat to Simulate?

1Select the study upon which the solver is defined.

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Simulating the ModelSimulation Settings

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Simulator Editor

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Which study and then experiments to simulate

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Simulation parameters. Further described in the lecture. Please consult the Möbius Manual for details.

4The way the result is stored

Simulating the ModelUsing your Neighbors

1 Simulation tasks can be distributed into available neighboring computers

2Available Neighbors

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Those who are willing to help

Simulating the ModelDon’t Forget to Save

1Saving the simulation settings. Here, saving is not compiling.

You can start the simulation now.

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Simulating the ModelRunning the Simulation

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Experiments that will be run.

The progress of the simulation of the experiment. Blue colored means “has converged”. Red colored means “has not converged”.

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Simulating the ModelThe Result of the Simulation

The detailed result (time, mean and variance of variables, settings used, etc.) is shown here.

Outline


That’s It!

möbius tutorial - fmtfmt.cs.utwente.nl/tools/motor/misc_doc/mobiustutorial.pdf · möbius tutorial...

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