got some better assignment

scheduler/preprocessing.py

@@ -85,6 +85,35 @@ def assign_tasks(solution_hours, solution, durations, machine_count):
     return
 
 
+def getMinRuntime(processes, machine):
+    min_time = 9999999999
+    for process in processes:
+        if min_time > process[machine]:
+            min_time = process[machine]
+    return min_time
+
+
+def get_batch_durations(durations):
+    batch_durations = []
+    batch_durations.append(calculate_batch_duration([0, 7], durations))
+    batch_durations.append(calculate_batch_duration([1, 8], durations))
+    batch_durations.append(calculate_batch_duration([2, 9], durations))
+    batch_durations.append(calculate_batch_duration([3, 10], durations))
+    batch_durations.append(calculate_batch_duration([4, 11], durations))
+    batch_durations.append(calculate_batch_duration([5, 12], durations))
+    batch_durations.append(calculate_batch_duration([6, 13], durations))
+    batch_durations.append(calculate_batch_duration([14], durations))
+    batch_durations.append(calculate_batch_duration([15, 22], durations))
+    batch_durations.append(calculate_batch_duration([16, 23], durations))
+    batch_durations.append(calculate_batch_duration([17, 24], durations))
+    batch_durations.append(calculate_batch_duration([18, 25], durations))
+    batch_durations.append(calculate_batch_duration([19, 26], durations))
+    batch_durations.append(calculate_batch_duration([20, 27], durations))
+    batch_durations.append(calculate_batch_duration([21, 28], durations))
+    batch_durations.append(calculate_batch_duration([29], durations))
+
+    return batch_durations
+
 def best_performance_under_limit(
         max_width: int,
         prices: List[float],
@@ -108,23 +137,7 @@ def best_performance_under_limit(
     #
     if strategy == "Cheapest":
         # information extrapolated by drawing a dot graph, keep strongly tied functions together
-        batch_durations = []
-        batch_durations.append(calculate_batch_duration([0, 7], durations))
-        batch_durations.append(calculate_batch_duration([1, 8], durations))
-        batch_durations.append(calculate_batch_duration([2, 9], durations))
-        batch_durations.append(calculate_batch_duration([3, 10], durations))
-        batch_durations.append(calculate_batch_duration([4, 11], durations))
-        batch_durations.append(calculate_batch_duration([5, 12], durations))
-        batch_durations.append(calculate_batch_duration([6, 13], durations))
-        batch_durations.append(calculate_batch_duration([14], durations))
-        batch_durations.append(calculate_batch_duration([15, 22], durations))
-        batch_durations.append(calculate_batch_duration([16, 23], durations))
-        batch_durations.append(calculate_batch_duration([17, 24], durations))
-        batch_durations.append(calculate_batch_duration([18, 25], durations))
-        batch_durations.append(calculate_batch_duration([19, 26], durations))
-        batch_durations.append(calculate_batch_duration([20, 27], durations))
-        batch_durations.append(calculate_batch_duration([21, 28], durations))
-        batch_durations.append(calculate_batch_duration([29], durations))
+        batch_durations = get_batch_durations(durations)
 
         batch_costs = cost_per_process_and_machine(batch_durations, prices)
 
@@ -166,6 +179,7 @@ def best_performance_under_limit(
         #write solution
         current_price = max_price
         current_solution[solutions_machine[max_price_position]] = math.ceil(solutions_hours[max_price_position]/3600 * prices[solutions_machine[max_price_position]])
+        batch_durations = get_batch_durations(durations)
 
         index = 0
         for solution in solutions_hours:
@@ -175,6 +189,8 @@ def best_performance_under_limit(
             local_price = math.ceil(solution / 3600) * prices[solutions_machine[index]]
             factor = math.floor(max_price / local_price)
             new_runtime = solution / factor
+            if new_runtime > getMinRuntime(batch_durations, index):
+                new_runtime = getMinRuntime(batch_durations, index)
             new_price = math.ceil(solution / 3600 /factor ) * prices[solutions_machine[index]] * factor
             if (new_runtime < solutions_hours[max_price_position] * 0.9): # 0.9 to simulate the traffic time needed to transfer
                 current_price = new_price
@@ -188,25 +204,7 @@ def best_performance_under_limit(
     # The calculation only uses homogeneous VM distribution currently
 
 
-    # Greedy
-    batch_durations = []
-    batch_durations.append(calculate_batch_duration([0, 7], durations))
-    batch_durations.append(calculate_batch_duration([1, 8], durations))
-    batch_durations.append(calculate_batch_duration([2, 9], durations))
-    batch_durations.append(calculate_batch_duration([3, 10], durations))
-    batch_durations.append(calculate_batch_duration([4, 11], durations))
-    batch_durations.append(calculate_batch_duration([5, 12], durations))
-    batch_durations.append(calculate_batch_duration([6, 13], durations))
-    batch_durations.append(calculate_batch_duration([14], durations))
-    batch_durations.append(calculate_batch_duration([15, 22], durations))
-    batch_durations.append(calculate_batch_duration([16, 23], durations))
-    batch_durations.append(calculate_batch_duration([17, 24], durations))
-    batch_durations.append(calculate_batch_duration([18, 25], durations))
-    batch_durations.append(calculate_batch_duration([19, 26], durations))
-    batch_durations.append(calculate_batch_duration([20, 27], durations))
-    batch_durations.append(calculate_batch_duration([21, 28], durations))
-    batch_durations.append(calculate_batch_duration([29], durations))
-
+    batch_durations = get_batch_durations(durations)
     assign_tasks(solutions_hours[max_price_position], solutions_machine[max_price_position], batch_durations, current_solution[solutions_machine[max_price_position]])
 
     return current_price, current_solution