EMAM2Middleware

Purpose

This generator takes an EMAM or EMADL model and connects it to a middleware library. If all Ports of two connected Components are marked as middleware Ports, the generator will create 2 executables that can be deployed on different machines. All communication of these 2 Components will then be tunneled trough the specified middleware: It also supports automatic clustering of the subcomponents to deploy on different machines.

Other important documents

Quickstart

If you want to use the generator for your project, check out QUICKSTART_USER.md.

If you want to add features to this generator, check out QUICKSTART_DEVELOPER.md

Writing your own Middleware Generator

see TUTORIAL_ADD_MIDDLEWARE.md

Dependencies needed to compile the generated projects

See INSTALL_DEPENDENCIES.md

Usage

CLI

Maven generates the jar embedded-montiarc-math-middleware-generator-{Version}-jar-with-dependencies.jar and the cli is located in de.monticore.lang.monticar.generator.middleware.cli.DistributedTargetGeneratorCli.

Parameters: ${file path to config json} OR -r ${raw json config string}

Example: CliUsage.sh

An example config file with all clustering algorithms: config

Clustering Parameters:

There are 4 different Clustering Algorithms with distinct parameters

Every parameter of the clustering algorithms can be dynamic, enabling automatic search for the best values. Available are lists and generators as seen in the example below:

"sigma":[1,2,3]
"sigma":{
  "min":1,
  "max":3,
  "step":1
}
"sigma":{
  "min":1,
  "max":3,
  "count":3
}

Also see clusterDynamic.json and clusterDynamicList.json

Spectral Clustering:

DBScan:

Markov:

Affinity Propagation:

Visulization of clustering results

There are 3 scripts available to visualise the results of the clustering process. They all create graphs for each of the 4 evaluation models:

1. evaluationVisualisation.py: bar graphs that compare the size of clusters, distance score, and time taken in ms
2. montecarlovisualisation.py: line graph visualising the average distance cost for random clustering(with Monte Carlo)
3. silhouetteVisualisation.py: point graph visualising the silhouette score of different clusterings sorted by cluster size

Before using them install Python 3+ and the packages matplotlib and numpy.

After running EvaluationTest(Warning: very long runtime) you can visualise the results by calling(from the project root):

python3 src/test/resources/evaluationVisualisation.py target/evaluation/autopilot/emam/clusteringResults.json target/evaluation/pacman/emam/clusteringResults.json target/evaluation/supermario/emam/clusteringResults.json target/evaluation/daimler/emam/clusteringResults.json

or

python3 src/test/resources/montecarlovisualisation.py target/evaluation/autopilotMC/monteCarloResults.json target/evaluation/pacmanMC/monteCarloResults.json target/evaluation/supermarioMC/monteCarloResults.json target/evaluation/daimlerMC/monteCarloResults.json

or

python3 src/test/resources/silhouetteVisualisation.py target/evaluation/autopilotSilhouette/emam/clusteringResults.json target/evaluation/pacmanSilhouette/emam/clusteringResults.json target/evaluation/supermarioSilhouette/emam/clusteringResults.json target/evaluation/daimlerSilhouette/emam/clusteringResults.json

Defining the connection between a component and the middleware

The connection between middleware and the component is defined as tags on Ports in .tag files.

Example with ROS Middleware:

Tags of the type RosConnection can either be simple tags(see Example 3) or define a topic(http://wiki.ros.org/Topics) with name, type and optional msgField(http://wiki.ros.org/msg , 2.) Examples:

1. src/test/resources/tests/a/Add.tag
2. src/test/resources/tests/a/Echo.tag
3. src/test/resources/tests/dist/SimpleDist.tag

Use-case 1: Creating 1 executable

Look at GenerationTest::testMiddlewareGenerator. The component is defined in src/test/resources/tests/a/AddComp.emam and the tags for the connection to ros are defined in src/test/resources/tests/a/Add.tag

Use-case 2: Creating multiple executables for distributed systems

Look at GenerationTest::testDistributedTargetGenerator. The component is defined in src/test/resources/dist/DistComp.emam and the tags for the connection to ros are defined in src/test/resources/tests/dist/SimpleDist.tag

Running the Integration tests locally

To run the integration tests locally, docker needs to be installed. Install instructions can be found here.

Run the tests by executing dockerLocalIntegrationTestRos.sh or dockerLocalIntegrationTestRos2.sh as root:

sudo src/test/bash/dockerLocalIntegrationTestRos.sh
sudo src/test/bash/dockerLocalIntegrationTestRos2.sh