Implement Benchmarking Strategy: Heuristic 1 (Step Strategy)

Please review @she Closes #48 (closed) Tested with unit-test with a complex input and with manual test for the following two workloads: (1 = successful experiment, in the sense that the application could handle the load, 0 = unsuccessful experiment):

Manual Workload 1

dim_value/instances	1	2
100	1	1
200	1	1

The result of the execution was as follows (executed experiments marked with 'x', not executed with 'o'):

dim_value/instances	1	2
100	x	o
200	x	o

Manual Workload 2

dim_value/instances	1	2
100	1	1
300.000	0	0

The result of the execution was as follows (executed experiments marked with 'x', not executed with 'o'):

dim_value/instances	1	2
100	x	o
300.000	x	x

execution/README.md

@@ -156,3 +156,11 @@ The `./theodolite.sh` is the entrypoint for all benchmark executions. Is has to
 * `<memory-limit>`: Kubernetes memory limit. Optional. Default `4Gi`.
 * `<commit-interval>`: Kafka Streams' commit interval in milliseconds. Optional. Default `100`.
 * `<duration>`: Duration in minutes subexperiments should be executed for. Optional. Default `5`.
+* `<strategy>`: The benchmarking strategy. Can be set to `default` or `step`. For more details see Section _Benchmarking Strategies_. Default `default`.
+
+### <a name="benchmarking-strategies"></a>Benchmarking Strategies
+There are the following benchmarking strategies:
+
+* `default`: Execute a subexperiment for each combination of the number of replicas (N), and the number of workload intensities (M). The amount of executed subexperiments is N*M.
+* `step`: After each subexperiment, it is checked, whether the application could handle the workload. If the workload could be handled, the workload intensity is increased in the next subexperiment. Otherwise, the workload intensity is kept the same and the number of instances is increased. The amount of executed subexperiments is at most N+M-1.
+