Resolve "Implement Quarkus/Kotlin protype"

This MR resolves most of #109 (closed) and contains the first part of a prototype for a theodolite implementation in Kotlin.

At the moment, this MR contains the following parts:

• #129 (closed) Implementation for search strategies and restrictions
  • Linear search
  • Binary search
  • lower bound restriction
  • simple unit test
• #133 (closed) (merged with !82 (merged)) Adds a logging capability
• #130 (closed) Features of run_uc -> #126 (closed) and #127 (closed)
• #127 (closed) (merged with !79 (closed))
  • Communication with zookeeper, for example for resetting after a experiment run
• #126 (closed) (merged with !81 (closed))
  • Communication with Kafka, for example to create or delete Topics
• an execution package as a starting point for running theodolite
• ability to load Kubernetes resource configuration as yaml files, configure the loaded resource and deploy the modified resources to K8s. And, based on this, the implementation of the benchmark UC1 (I couldn't find a ticket for this, is there one already @stu203404)

Todos before ready to merge:

• apply open reviews (mostly for @stu203404)
• apply open reviews (mostly for @stu200776 and @stu126940)
• replace print statement with logger calls to fully resolve #133 (closed)
• finalize exemplary implementation of UC1
• maybe we should add a basic implementation of the SLOChecker (#135 (closed)) and the MetricFetcher (#139 (closed)) before merge moved to #135 (closed) and #139 (closed)

This MR closes:

Closes #126 (closed), Closes #127 (closed), Closes #133 (closed), Closes #129 (closed)

analysis/demand-metric.ipynb

 %% Cell type:markdown id: tags:
 
 # Theodolite Analysis - Demand Metric
 
 This notebook allows applies Theodolite's *demand* metric to describe scalability of a SUT based on Theodolite measurement data.
 
 Theodolite's *demand* metric is a function, mapping load intensities to the minimum required resources (e.g., instances) that are required to process this load. With this notebook, the *demand* metric function is approximated by a map of tested load intensities to their minimum required resources.
 
 The final output when running this notebook will be a CSV file, providig this mapping. It can be used to create nice plots of a system's scalability using the `demand-metric-plot.ipynb` notebook.
 
 %% Cell type:markdown id: tags:
 
 In the following cell, we need to specifiy:
 
 * `exp_id`: The experiment id  that is to be analyzed.
 * `warmup_sec`: The number of seconds which are to be ignored in the beginning of each experiment.
 * `max_lag_trend_slope`: The maximum tolerable increase in queued messages per second.
 * `measurement_dir`: The directory where the measurement data files are to be found.
 * `results_dir`: The directory where the computed demand CSV files are to be stored.
 
 %% Cell type:code id: tags:
 
 ``` python
 exp_id = 200
 warmup_sec = 60
 max_lag_trend_slope = 2000
 measurement_dir = '<path-to>/measurements'
 results_dir = '<path-to>/results'
 ```
 
 %% Cell type:markdown id: tags:
 
 With the following call, we compute our demand mapping.
 
 %% Cell type:code id: tags:
 
 ``` python
 from src.demand import demand
 
 demand = demand(exp_id, measurement_dir, max_lag_trend_slope, warmup_sec)
 ```
 
 %% Cell type:markdown id: tags:
 
 We might already want to plot a simple visualization here:
 
 %% Cell type:code id: tags:
 
 ``` python
 demand.plot(kind='line',x='load',y='resources')
 ```
 
 %% Cell type:markdown id: tags:
 
 Finally we store the results in a CSV file.
 
 %% Cell type:code id: tags:
 
 ``` python
+import os
+
 demand.to_csv(os.path.join(results_dir, f'exp{exp_id}_demand.csv'), index=False)
 ```