Documentation update (#261)

* Updated a lot of documentation, added a new get-started tutorial.

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+OpenDC provides three types of failure models: [Trace-based](#trace-based-failure-models), [Sample-based](#sample-based-failure-models),
+and [Prefab](#prefab-failure-models).
+
+All failure models have a similar structure containing three simple steps.
+
+1. The _interval_ time determines the time between two failures.
+2. The _duration_ time determines how long a single failure takes.
+3. The _intensity_ determines how many hosts are effected by a failure.
+
+:::info Code
+The code that defines the Failure Models can found [here](https://github.com/atlarge-research/opendc/blob/master/opendc-experiments/opendc-experiments-base/src/main/kotlin/org/opendc/experiments/base/experiment/specs/FailureModelSpec.kt).
+:::
+
+## Trace based failure models
+Trace-based failure models are defined by a parquet file. This file defines the interval, duration, and intensity of
+several failures. The failures defined in the file are looped. A valid failure model file follows the format defined below:
+
+| Metric            | Datatype   | Unit          | Summary                                    |
+|-------------------|------------|---------------|--------------------------------------------|
+| failure_interval  | int64      | milli seconds | The duration since the last failure        |
+| failure_duration  | int64      | milli seconds | The duration of the failure                |
+| failure_intensity | float64    | ratio         | The ratio of hosts effected by the failure |
+
+:::info Code
+The code implementation of Trace Based Failure Models can be found [here](https://github.com/atlarge-research/opendc/blob/master/opendc-compute/opendc-compute-failure/src/main/kotlin/org/opendc/compute/failure/models/TraceBasedFailureModel.kt)
+:::
+
+### Example
+A trace-based failure model is specified by setting "type" to "trace-based".
+After, the user can define the path to the failure trace using "pathToFile":
+```json
+{
+    "type": "trace-based",
+    "pathToFile": "path/to/your/failure_trace.parquet"
+}
+```
+
+The "repeat" value can be set to false if the user does not want the failures to loop:
+```json
+{
+    "type": "trace-based",
+    "pathToFile": "path/to/your/failure_trace.parquet",
+    "repeat": "false"
+}
+```
+
+## Sample based failure models
+Sample based failure models sample from three distributions to get the _interval_, _duration_, and _intensity_ of
+each failure. Sample-based failure models are effected by randomness and will thus create different results based
+on the provided seed.
+
+:::info Code
+The code implementation for the Sample based failure models can be found [here](https://github.com/atlarge-research/opendc/blob/master/opendc-compute/opendc-compute-failure/src/main/kotlin/org/opendc/compute/failure/models/SampleBasedFailureModel.kt)
+:::
+
+### Distributions
+OpenDC supports eight different distributions based on java's [RealDistributions](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/RealDistribution.html).
+Because the different distributions require different variables, they have to be specified with a specific "type".
+Next, we show an example of a correct specification of all available distributions in OpenDC.
+
+#### [ConstantRealDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/ConstantRealDistribution.html)
+
+```json
+{
+    "type": "constant",
+    "value": 10.0
+}
+```
+
+#### [ExponentialDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/ExponentialDistribution.html)
+```json
+{
+    "type": "exponential",
+    "mean": 1.5
+}
+```
+
+#### [GammaDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/GammaDistribution.html)
+```json
+{
+    "type": "gamma",
+    "shape": 1.0,
+    "scale": 0.5
+}
+```
+
+#### [LogNormalDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/LogNormalDistribution.html)
+```json
+{
+    "type": "log-normal",
+    "scale": 1.0,
+    "shape": 0.5
+}
+```
+
+#### [NormalDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/NormalDistribution.html)
+```json
+{
+    "type": "normal",
+    "mean": 1.0,
+    "std": 0.5
+}
+```
+
+#### [ParetoDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/ParetoDistribution.html)
+```json
+{
+    "type": "pareto",
+    "scale": 1.0,
+    "shape": 0.6
+}
+```
+
+#### [UniformRealDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/UniformRealDistribution.html)
+```json
+{
+    "type": "constant",
+    "lower": 5.0,
+    "upper": 10.0
+}
+```
+
+#### [WeibullDistribution](https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/org/apache/commons/math3/distribution/WeibullDistribution.html)
+```json
+{
+    "type": "constant",
+    "alpha": 0.5,
+    "beta": 1.2
+}
+```
+
+### Example
+A sample-based failure model is defined using three distributions for _intensity_, _duration_, and _intensity_.
+Distributions can be mixed however the user wants. Note, values for _intensity_ and _duration_ are clamped to be positive.
+The _intensity_ is clamped to the range [0.0, 1.0).
+To specify a sample-based failure model, the type needs to be set to "custom".
+
+Example:
+```json
+{
+    "type": "custom",
+    "iatSampler": {
+        "type": "exponential",
+        "mean": 1.5
+    },
+    "durationSampler": {
+        "type": "constant",
+        "alpha": 0.5,
+        "beta": 1.2
+    },
+    "nohSampler": {
+        "type": "constant",
+        "value": 0.5
+    }
+}
+```
+
+## Prefab failure models
+The final type of failure models is the prefab models. These are models that are predefined in OpenDC and are based on
+research. Currently, OpenDC has 9 prefab models based on [The Failure Trace Archive: Enabling the comparison of failure measurements and models of distributed systems](https://www-sciencedirect-com.vu-nl.idm.oclc.org/science/article/pii/S0743731513000634)
+The figure below shows the values used to define the failure models.
+![img.png](img.png)
+
+Each failure model is defined four times, on for each of the four distribution.
+The final list of available prefabs is thus:
+
+    G5k06Exp
+    G5k06Wbl
+    G5k06LogN
+    G5k06Gam
+    Lanl05Exp
+    Lanl05Wbl
+    Lanl05LogN
+    Lanl05Gam
+    Ldns04Exp
+    Ldns04Wbl
+    Ldns04LogN
+    Ldns04Gam
+    Microsoft99Exp
+    Microsoft99Wbl
+    Microsoft99LogN
+    Microsoft99Gam
+    Nd07cpuExp
+    Nd07cpuWbl
+    Nd07cpuLogN
+    Nd07cpuGam
+    Overnet03Exp
+    Overnet03Wbl
+    Overnet03LogN
+    Overnet03Gam
+    Pl05Exp
+    Pl05Wbl
+    Pl05LogN
+    Pl05Gam
+    Skype06Exp
+    Skype06Wbl
+    Skype06LogN
+    Skype06Gam
+    Websites02Exp
+    Websites02Wbl
+    Websites02LogN
+    Websites02Gam
+
+:::info Code
+The different Prefab models can be found [here](https://github.com/atlarge-research/opendc/tree/master/opendc-compute/opendc-compute-failure/src/main/kotlin/org/opendc/compute/failure/prefab)
+:::
+
+### Example
+To specify a prefab model, the "type" needs to be set to "prefab".
+After, the prefab can be defined with "prefabName":
+
+```json
+{
+    "type": "prefab",
+    "prefabName": "G5k06Exp"
+}
+```
+