diff --git a/doc/horizontal.md b/doc/horizontal.md
index 863b8c84c5173453e8299130c21fee81144f260d..5217c1f97a07c01c9a38d2e2ae60c465fffefe08 100644
--- a/doc/horizontal.md
+++ b/doc/horizontal.md
@@ -1,33 +1,51 @@
-# Vertical Scaling Scenario
-In this scenario a hadoop cluster with one worker node getting monitored is deployed.
-As soon as the CPU utilization of the worker node reaches a critical level, a request against the OCCI
-API is performed, increasing the number of cores and memory available to the machine.
-
-## Instructions
-In order to provide an easy use of the MART Server, a docker container embedding the server and all required plugins is provided.
-However, to use specialized plugins currently the docker image has to be recreated. Thus, a brief explanation of the single steps are given in the following:
-
-1. Install [docker](https://docs.docker.com/install/)
-2. Clone the [MOCCI repository](https://gitlab.gwdg.de/rwm/de.ugoe.cs.rwm.mocci)
-3. Optional: Add martserver-plugins and roles to be used by the server. Adjust the authorized_keys file for ssh access.
-4. Navigate to src/test/resources/
-5. Create docker image: sudo docker build -t mart-server .
-6. Test the docker image: sudo docker run -p 8080:8080 -p 22:22 mart-server
-7. Store the docker image: sudo docker save mart-server $>$ mart-server.tar
-8. To access the container you can use an ssh connection: ssh -i \$key root@localhost
-
-To build this container a fatjar of the MartServer is used. To use newer versions please refer to the [documentation of the MartServer](https://github.com/occiware/MartServer/blob/master/doc/server.md) in how to create a docker container.
-
-## Loading a Docker Container
-To initialize the proposed OCCI extensions, the following plugins need to be added to the OCCI-Studio. 
-These allow to correctly depict OCCI models in the textual and graphical editor. To Install plugins the following steps have
-to be performed:
-
-1. Download/Navigate to the archive containing the docker image
-2. Load the docker image: docker load $<$ mart-server.tar
-3. Start the image: sudo docker run -p 8080:8080 -p 22:22 mart-server
-4. Start with bash: sudo docker run -p 8080:8080 -p 22:22 -i -t mart-server /bin/bash
-5. To access the container you can use an ssh connection: ssh -i \$key root@localhost
-
-## Configuring the MartServer to be used in OpenStack
-[Documentation on how to setup and configure the MartServer for an OpenStack Cloud](doc/openstack.md)
\ No newline at end of file
+# Horizontal Scaling Scenario
+In this scenario a hadoop cluster with one worker node is deployed and scaled according to gathered CPU utilization.
+Therefore, a MAPE-k loop is initialized that periodically checks the CPU utilization of all worker nodes.
+Thereafter, it is decided whether additional worker nodes are required (scaleUp) or not (scaleDown).
+When a scaleUp is performed, a compute node hosting a hadoop-worker component is added to the model,
+which gets executed over a [models at runtime engine](https://gitlab.gwdg.de/rwm/de.ugoe.cs.rwm.docci).
+
+
+##Deploying the Cluster
+First, the hadoop cluster has to be deployed. Therefore, first start the MartServer.
+If the getting started VM is used the following script can be executed:
+```
+./startMART.sh
+```
+*Note:* If this scenario is not performed in a running cloud environment consider executing the resetMart.sh script first.
+
+Thereafter, start the InitialDeployment.java file as an Java Application.
+If the VM is used: Open a terminal and navigate to the VM's desktop and execute the initialDeployment.jar. 
+```
+java -jar initialDeployment.jar
+```
+
+After the deployment has been performed you can investigate the deployed OCCI model by opening your browser and query for OCCI entitites:
+```
+http://localhost:8080/compute
+http://localhost:8080/sensor
+http://localhost:8080/monitorableproperty
+```
+
+##Starting the MAPE-K loop
+To start the MAPE-K loop execute MAPE.java as a Java Application.
+In the VM it is located on the desktop.
+```
+java -jar MAPE.jar
+```
+In this scenario, a Java application is started that utilizes the schematized data format of the OCCI metamodel and its extensions.
+This scenario serves as an example on how to work with the OCCI ecosystem, including the monitoring extension and the models at runtime engine.
+
+*Note:* To perform this scenario in a running cloud multiple adjustments have to be performed. Please refer to this [documentation](doc/openstack) to get started with actual cloud deployments.
+
+##Tuning the Scenario
+Again the simulated monitoring results can be adjusted as described in Section "Tuning the Scenario" of the [first scenario](doc/vertical.md).
+Moreover, to investigate what is happening in this scenario it is recommended to open the OCCI-Studio and execute the scenario from here.
+The class is located at:
+```
+de.ugoe.cs.rwm.mocci/src/main/java/MAPE.java
+```
+To log specific information, the logger setup, found in RegistryAndLoggerSetup.java, can be manipulated.
+Especially, the Executor logger is interesting as it shows the performed REST requests against the OCCI API.
+
+*Note:* These can be copy and pasted in order to perform manual requests using the terminal. E.g.: curl -v -X PUT ...
\ No newline at end of file
diff --git a/doc/vertical.md b/doc/vertical.md
index 717ac8ecded7cdf67d38543473e1beaff72849ad..0ae70f7128f405c42264a6de0d0044da8b0e4f5d 100644
--- a/doc/vertical.md
+++ b/doc/vertical.md
@@ -1,33 +1,56 @@
-# Setting up the MartServer
-The MART Server implements the OCCI API used to orchestrate the Cloud deployments.
-This is major component serving as entry point for our application.
-In the following a description of how a Docker container for the MART Server can be created, stored, loaded, and started.
-
-## Creating a Docker Container
-In order to provide an easy use of the MART Server, a docker container embedding the server and all required plugins is provided.
-However, to use specialized plugins currently the docker image has to be recreated. Thus, a brief explanation of the single steps are given in the following:
-
-1. Install [docker](https://docs.docker.com/install/)
-2. Clone the [MOCCI repository](https://gitlab.gwdg.de/rwm/de.ugoe.cs.rwm.mocci)
-3. Optional: Add martserver-plugins and roles to be used by the server. Adjust the authorized_keys file for ssh access.
-4. Navigate to src/test/resources/
-5. Create docker image: sudo docker build -t mart-server .
-6. Test the docker image: sudo docker run -p 8080:8080 -p 22:22 mart-server
-7. Store the docker image: sudo docker save mart-server $>$ mart-server.tar
-8. To access the container you can use an ssh connection: ssh -i \$key root@localhost
-
-To build this container a fatjar of the MartServer is used. To use newer versions please refer to the [documentation of the MartServer](https://github.com/occiware/MartServer/blob/master/doc/server.md) in how to create a docker container.
-
-## Loading a Docker Container
-To initialize the proposed OCCI extensions, the following plugins need to be added to the OCCI-Studio. 
-These allow to correctly depict OCCI models in the textual and graphical editor. To Install plugins the following steps have
-to be performed:
-
-1. Download/Navigate to the archive containing the docker image
-2. Load the docker image: docker load $<$ mart-server.tar
-3. Start the image: sudo docker run -p 8080:8080 -p 22:22 mart-server
-4. Start with bash: sudo docker run -p 8080:8080 -p 22:22 -i -t mart-server /bin/bash
-5. To access the container you can use an ssh connection: ssh -i \$key root@localhost
-
-## Configuring the MartServer to be used in OpenStack
-[Documentation on how to setup and configure the MartServer for an OpenStack Cloud](doc/openstack.md)
\ No newline at end of file
+# Vertical Scaling Scenario
+In this scenario a hadoop cluster with one worker node getting monitored is deployed.
+Thereafter, a MAPE-k loop is initialized that periodically checks whether the CPU utilization of the worker node reaches a critical level.
+If that is the case a request against the OCCI API is performed, increasing the number of cores and memory available to the machine.
+
+
+##Deploying the Cluster
+First, the hadoop cluster has to be deployed. Therefore, first start the MartServer.
+If the getting started VM is used the following script can be executed:
+```
+./startMART.sh
+```
+*Note:* If this scenario is not performed in a running cloud environment consider executing the resetMart.sh script first.
+
+Thereafter, start the InitialDeployment.java file as an Java Application.
+If the VM is used: Open a terminal and navigate to the VM's desktop and execute the initialDeployment.jar. 
+```
+java -jar initialDeployment.jar
+```
+
+After the deployment has been performed you can investigate the deployed OCCI model by opening your browser and query for OCCI entitites:
+```
+http://localhost:8080/compute
+http://localhost:8080/sensor
+http://localhost:8080/monitorableproperty
+```
+
+##Starting the Adaptation Script
+To start the script execute the vertical.sh script.
+In the VM it is located on the desktop, otherwise you can find it [here](https://gitlab.gwdg.de/rwm/de.ugoe.cs.rwm.mocci/blob/master/src/main/resources/vertical.sh).
+```
+./vertical.sh
+```
+In this scenario, a simple bash script is used to check the gathered monitoring data and perform corresponding actions.
+This scenario serves as an example on how to directly work with the OCCI API, including the monitoring extension, by simply writing small bash scripts.
+
+*Note:* This scenario mainly serves to get started with the OCCI API. Currently, there is no connector implementing the vertical adjustment as shown in this scenario.
+
+##Tuning the Scenario
+As the adaptation only reacts on a Critical behavior, it may be interesting to tune the simulated monitoring results.
+Therefore, the following steps have to be performed:
+1. Stop the MartServer (CTRL-C)
+2. Navigate to ~/martserver-plugins
+3. Open the de.ugoe.cs.rwm.mocci.connecter.dummy.jar with the archive manager.
+4. Doubleclick on the resultprovider.properties file
+5. Adjust the values to your liking
+
+The file contains the following:
+```
+CPU = None,Low,Medium,High,Critical,5000
+```
+* CPU: Represents the monitorable.property to be adjusted.
+* 5000: Represents the interval in which monitoring.results are written.
+* None-Critical: Represents the simulated monitoring.results. 
+
+*Note;* If you want to execute the second scenario please bring the resultprovider.properties file to its original state. 
\ No newline at end of file
diff --git a/src/main/resources/mape.sh b/src/main/resources/vertical.sh
similarity index 100%
rename from src/main/resources/mape.sh
rename to src/main/resources/vertical.sh