Introduce new Benchmark class and Patcher

This MR covers most of #151 (closed)

In detail, it includes:

• Introduction of the new benchmark and patcher types defined in #151 (closed), closes #142 (closed)
• Refactoring of many components, e.g. the K8s manager according to #151 (closed), closes #143 (closed)
• Allow setting arbitrary environment parameters, close mostly #98 (closed)
• Load configuration based on two YAML files, this closes the first part of #137 (closed)
• Allow to set Kafka and Zookeeper service names, closes #122 (closed)
• Usage of the new Workload Generator, closes #157 (closed)

TODOs before ready to merge:

• Remove hard coded parts, e.g. type of instances and loads, which is not actually loaded by configuration, see Resource and LoadDimension class in kotlin.
• Change ("patching") methods from the Kubernetes managers to actual patchers to fully resolve #143 (closed)
• Test everything inside Kubernetes.
• Clean up, remove old classes and benchmark types
• Add a NodeSelector Patcher to resolve #93 (closed)
• Maybe we need more Patchers to fully resolve #98 (closed), if more of the non environment parameters (such as K8s ResourceLimits, K8s Labels, ..) must be configurable (@she ?)
• Make namespace configurable moved to #155 (closed)

Feel free to expand this description and add TODOs

analysis/demand-metric-plot.ipynb

 %% Cell type:markdown id: tags:
 
 # Theodolite Analysis - Plotting the Demand Metric
 
 This notebook creates a plot, showing scalability as a function that maps load intensities to the resources required for processing them. It is able to combine multiple such plots in one figure, for example, to compare multiple systems or configurations.
 
 The notebook takes a CSV file for each plot mapping load intensities to minimum required resources, computed by the `demand-metric-plot.ipynb` notebook.
 
 %% Cell type:markdown id: tags:
 
 First, we need to import some libraries, which are required for creating the plots.
 
 %% Cell type:code id: tags:
 
 ``` python
 import os
 import pandas as pd
 from functools import reduce
 import matplotlib.pyplot as plt
 from matplotlib.ticker import FuncFormatter
 from matplotlib.ticker import MaxNLocator
 ```
 
 %% Cell type:markdown id: tags:
 
 We need to specify the directory, where the demand CSV files can be found, and a dictionary that maps a system description (e.g. its name) to the corresponding CSV file (prefix). To use Unicode narrow non-breaking spaces in the description format it as `u"1000\u202FmCPU"`.
 
 %% Cell type:code id: tags:
 
 ``` python
 results_dir = '<path-to>/results'
 
 experiments = {
     'System XYZ': 'exp200',
 }
 ```
 
 %% Cell type:markdown id: tags:
 
 Now, we combie all systems described in `experiments`.
 
 %% Cell type:code id: tags:
 
 ``` python
 dataframes = [pd.read_csv(os.path.join(results_dir, f'{v}_demand.csv')).set_index('load').rename(columns={"resources": k}) for k, v in experiments.items()]
 
 df = reduce(lambda df1,df2: df1.join(df2,how='outer'), dataframes)
 ```
 
 %% Cell type:markdown id: tags:
 
 We might want to display the mappings before we plot it.
 
 %% Cell type:code id: tags:
 
 ``` python
 df
 ```
 
 %% Cell type:markdown id: tags:
 
 The following code creates a MatPlotLib figure showing the scalability plots for all specified systems. You might want to adjust its styling etc. according to your preferences. Make sure to also set a filename.
 
 %% Cell type:code id: tags:
 
 ``` python
 plt.style.use('ggplot')
+plt.rcParams['pdf.fonttype'] = 42 # TrueType fonts
+plt.rcParams['ps.fonttype'] = 42 # TrueType fonts
 plt.rcParams['axes.facecolor']='w'
 plt.rcParams['axes.edgecolor']='555555'
 #plt.rcParams['ytick.color']='black'
 plt.rcParams['grid.color']='dddddd'
 plt.rcParams['axes.spines.top']='false'
 plt.rcParams['axes.spines.right']='false'
 plt.rcParams['legend.frameon']='true'
 plt.rcParams['legend.framealpha']='1'
 plt.rcParams['legend.edgecolor']='1'
 plt.rcParams['legend.borderpad']='1'
 
 @FuncFormatter
 def load_formatter(x, pos):
     return f'{(x/1000):.0f}k'
 
 markers = ['s', 'D', 'o', 'v', '^', '<', '>', 'p', 'X']
 
 def splitSerToArr(ser):
     return [ser.index, ser.as_matrix()]
 
 plt.figure()
 #plt.figure(figsize=(4.8, 3.6)) # For other plot sizes
 #ax = df.plot(kind='line', marker='o')
 for i, column in enumerate(df):
     plt.plot(df[column].dropna(), marker=markers[i], label=column)
 plt.legend()
 ax = plt.gca()
 #ax = df.plot(kind='line',x='dim_value', legend=False, use_index=True)
 ax.set_ylabel('number of instances')
 ax.set_xlabel('messages/second')
 ax.set_ylim(ymin=0)
 #ax.set_xlim(xmin=0)
 ax.yaxis.set_major_locator(MaxNLocator(integer=True))
 ax.xaxis.set_major_formatter(FuncFormatter(load_formatter))
 
 plt.savefig('temp.pdf', bbox_inches='tight')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```