Add binary search strategy

execution/strategies/strategies/binary_search_strategy.py

+import os
+from .config import SubexperimentConfig
+
+def searchTransition(config, replica_index, lower, upper, subexperiment_counter):
+    if lower==upper:
+        print(f"Run subexperiment {subexperiment_counter} with config {config.dim_values[lower]} {config.replicas[replica_index]}")
+        subexperiment_config = SubexperimentConfig(config.use_case, subexperiment_counter, config.dim_values[lower], config.replicas[replica_index], config.partitions, config.cpu_limit, config.memory_limit, config.kafka_streams_commit_interval_ms, config.execution_minutes)
+        config.subexperiment_executor.execute(subexperiment_config)
+        result = config.subexperiment_evaluator.execute()
+        if result==1: # successful, the upper neighbor must be not successful
+            return lower+1
+        else: # not successful
+            return lower
+    elif lower+1 == upper:
+        print(f"Run subexperiment {subexperiment_counter} with config {config.dim_values[lower]} {config.replicas[replica_index]}")
+        subexperiment_config = SubexperimentConfig(config.use_case, subexperiment_counter, config.dim_values[lower], config.replicas[replica_index], config.partitions, config.cpu_limit, config.memory_limit, config.kafka_streams_commit_interval_ms, config.execution_minutes)
+        config.subexperiment_executor.execute(subexperiment_config)
+        result = config.subexperiment_evaluator.execute()
+        if result==1: # successful, the upper neighbor must be not successful
+            print(f"Run subexperiment {subexperiment_counter} with config {config.dim_values[upper]} {config.replicas[replica_index]}")
+            subexperiment_config = SubexperimentConfig(config.use_case, subexperiment_counter, config.dim_values[upper], config.replicas[replica_index], config.partitions, config.cpu_limit, config.memory_limit, config.kafka_streams_commit_interval_ms, config.execution_minutes)
+            config.subexperiment_executor.execute(subexperiment_config)
+            result = config.subexperiment_evaluator.execute()
+            if result == 1:
+                return upper+1
+            else:
+                return upper
+        else: # not successful
+            return lower
+    else:
+        # test mid
+        mid=(upper+lower)//2
+        print(mid)
+        print(f"Run subexperiment {subexperiment_counter} with config {config.dim_values[mid]} {config.replicas[replica_index]}")
+        subexperiment_config = SubexperimentConfig(config.use_case, subexperiment_counter, config.dim_values[mid], config.replicas[replica_index], config.partitions, config.cpu_limit, config.memory_limit, config.kafka_streams_commit_interval_ms, config.execution_minutes)
+        config.subexperiment_executor.execute(subexperiment_config)
+        result = config.subexperiment_evaluator.execute()
+        if result == 1: # success -> search in (mid+1, upper)
+            return searchTransition(config, replica_index, mid+1, upper, subexperiment_counter+1)
+        else: # not success -> search in (lower, mid-1)
+            return searchTransition(config, replica_index, lower, mid-1, subexperiment_counter+1)
+
+
+def execute(config):
+    subexperiment_counter=0
+    lower = 0
+    upper = len(config.dim_values)-1
+    j = 0
+    while j < len(config.replicas) and lower < len(config.dim_values):
+        lower = searchTransition(config, j, lower, upper, subexperiment_counter+1)
+        j+=1
+

execution/strategies/tests/test_binary_search_strategy.py

+import pprint
+
+from strategies.config import ExperimentConfig
+import strategies.strategies.binary_search_strategy as binary_search_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 ] 
+    ]
+
+expected_order = [
+        (3,0), # interval (0, 6)
+        (1,0), 
+        (0,0),
+        (3,1), # interval (0, 6)
+        (1,1),
+        (3,2), # interval (0, 6)
+        (1,2),
+        (2,2),
+        (4,3), # interval (3, 6)
+        (3,3),
+        (5,4), # interval (4, 6)
+        (4,4),
+        (5,5), # interval (5,6)
+        (5,6), # interval (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(last_experiment)
+    print("Index was expected to be:")
+    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_binary_search_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, binary_search_strategy, subexperiment_executor, subexperiment_evaluator)
+    executor = ExperimentExecutor(experiment_config)
+    executor.execute()
\ No newline at end of file

execution/strategies/tests/test_step_strategy.py

@@ -73,8 +73,6 @@ def subexperiment_evaluator_execute():
 subexperiment_evaluator.execute = subexperiment_evaluator_execute
 
 def test_step_strategy():
-    print("strt test")
-    assert True
     # declare parameters
     uc="test-uc"
     partitions=40

execution/theodolite.py

@@ -5,6 +5,7 @@ import os
 from strategies.config import ExperimentConfig
 import strategies.strategies.default_strategy as default_strategy
 import strategies.strategies.step_strategy as step_strategy
+import strategies.strategies.binary_search_strategy as binary_search_strategy
 from strategies.experiment_execution import ExperimentExecutor
 import strategies.subexperiment_execution.subexperiment_executor as subexperiment_executor
 import strategies.subexperiment_evaluation.noop_subexperiment_evaluator as noop_subexperiment_evaluator
@@ -25,6 +26,8 @@ print(f"Chosen benchmarking strategy: {benchmark_strategy}")
 if benchmark_strategy == "step":
     print(f"Going to execute at most {len(dim_values)+len(replicas)-1} subexperiments in total..")
     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)
+elif benchmark_strategy == "binary-search":
+    experiment_config = ExperimentConfig(uc, dim_values, replicas, partitions, cpu_limit, memory_limit, kafka_streams_commit_interval_ms, execution_minutes, binary_search_strategy, subexperiment_executor, subexperiment_evaluator)
 else:
     print(f"Going to execute {len(dim_values)*len(replicas)} subexperiments in total..")
     experiment_config = ExperimentConfig(uc, dim_values, replicas, partitions, cpu_limit, memory_limit, kafka_streams_commit_interval_ms, execution_minutes, default_strategy, subexperiment_executor, noop_subexperiment_evaluator)