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-### FailureModel
-The failure model that should be used during the simulation
-See [FailureModels](FailureModel.md) for detailed instructions.
-
-
-
-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.
-![failureModels.png](../../../static/img/failureModels.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"
-}
-```
-