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/strategies/tests/test_step_strategy.py

+import pprint
+
+from strategies.config import ExperimentConfig
+import strategies.strategies.step_strategy as step_strategy
+from strategies.experiment_execution import ExperimentExecutor
+import strategies.subexperiment_execution.subexperiment_executor as subexperiment_executor
+
+class Object(object):
+    pass
+
+pp = pprint.PrettyPrinter(indent=4)
+
+dim_values = [0, 1, 2, 3, 4, 5, 6]
+replicas = [0, 1, 2, 3, 4, 5, 6]
+
+# True means the experiment was successful
+# the experiments are indexed row (representing dimension values) and column (representing number of replicas) wise as usual arrays from 0 - 6 respectively.
+# this means the first row starts with (0,0), the second row with (1, 0) etc.
+successful = [
+       [ True , True , True , True , True , True , True  ],
+       [ False, False, True , True , True , True , True  ],
+       [ False, False, True , True , True , True , True  ],
+       [ False, False, False, True , True , True , True  ],
+       [ False, False, False, False, True , True , True  ],
+       [ False, False, False, False, False, False, True  ],
+       [ False, False, False, False, False, False, False ] 
+    ]
+
+# the expected order of executed experiments
+expected_order = [
+        (0,0),
+        (1,0),
+        (1,1),
+        (1,2),
+        (2,2),
+        (3,2),
+        (3,3),
+        (4,3),
+        (4,4),
+        (5,4),
+        (5,5),
+        (5,6),
+        (6,6)
+    ]
+
+last_experiment = (0, 0)
+experiment_counter = -1
+subexperiment_executor = Object()
+
+def subexperiment_executor_executor(config):
+    global experiment_counter, last_experiment, pp
+    print("Simulate subexperiment with config:")
+    pp.pprint(config)
+    last_experiment = (config.dim_value, config.replicas)
+    experiment_counter += 1
+    print("Simulation complete")
+
+subexperiment_executor.execute = subexperiment_executor_executor
+
+
+# returns True if the experiment was successful
+
+subexperiment_evaluator = Object()
+
+def subexperiment_evaluator_execute():
+    print("Evaluating last experiment. Index was:")
+    global expected_order, experiment_counter, last_experiment, successful
+    pp.pprint(expected_order[experiment_counter])
+    assert expected_order[experiment_counter] == last_experiment
+    print("Index was as expected. Evaluation finished.")
+    return 1 if successful[last_experiment[0]][last_experiment[1]] else 0
+
+subexperiment_evaluator.execute = subexperiment_evaluator_execute
+
+def test_step_strategy():
+    # declare parameters
+    uc="test-uc"
+    partitions=40
+    cpu_limit="1000m"
+    memory_limit="4Gi"
+    kafka_streams_commit_interval_ms=100
+    execution_minutes=5
+
+    # execute
+    experiment_config = ExperimentConfig(uc, dim_values, replicas, partitions, cpu_limit, memory_limit, kafka_streams_commit_interval_ms, execution_minutes, step_strategy, subexperiment_executor, subexperiment_evaluator)
+    executor = ExperimentExecutor(experiment_config)
+    executor.execute()
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