Merge branch '48-step-strategy' into 49-add-binary-search-strategy

execution/README.md

@@ -158,9 +158,9 @@ The `./theodolite.sh` is the entrypoint for all benchmark executions. Is has to
 * `<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
+### 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.
+* `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 always N*M.
+* `step`: A heuristic which works as follows: 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.
 

execution/strategies/tests/test_step_strategy.py

@@ -68,7 +68,7 @@ def subexperiment_evaluator_execute():
     pp.pprint(expected_order[experiment_counter])
     assert expected_order[experiment_counter] == last_experiment
     print("Index was as expected. Evaluation finished.")
-    return successful[last_experiment[0]][last_experiment[1]]
+    return 1 if successful[last_experiment[0]][last_experiment[1]] else 0
 
 subexperiment_evaluator.execute = subexperiment_evaluator_execute