first commit

main.py

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+# Import necessary libraries
+%pip install gurobipy
+import pandas as pd
+from gurobipy import *
+
+# Define model
+model = Model("CloudServiceTransportation")
+
+# Number of providers (AWS, Azure, Google)
+m = 3  # AWS, Azure, Google
+# Number of services (C1, C2, AI/ML)
+n = 3  # C1, C2, AI/ML
+
+# Cost coefficients for AWS, Azure, and Google for each service (no storage cost)
+costs_AWS = {'C1': 0.1664, 'C2': 0.08, 'AI/ML': 3.06}
+costs_Azure = {'C1': 0.2021, 'C2': 0.10, 'AI/ML': 0.90}
+costs_Google = {'C1': 0.1900, 'C2': 0.10, 'AI/ML': 1.80}
+
+# Supply limits for each provider (AWS, Azure, Google) - fixed units
+supply_AWS = {'C1': 100, 'C2': 300, 'AI/ML': 50}
+supply_Azure = {'C1': 120, 'C2': 180, 'AI/ML': 80}
+supply_Google = {'C1': 130, 'C2': 200, 'AI/ML': 70}
+
+# Demand for each service (C1, C2, AI/ML)
+demand = {'C1': 100, 'C2': 100, 'AI/ML': 50}
+
+# Inter-provider egress costs
+inter_provider_egress = {
+    ('AWS', 'Azure'): 20,
+    ('AWS', 'Google'): 30,
+    ('Azure', 'Google'): 15,
+    ('Azure', 'AWS'): 25,
+    ('Google', 'AWS'): 35,
+    ('Google', 'Azure'): 10
+}
+
+# Budget constraint
+budget = 20000  # Total budget
+
+# Decision variables for quantities to transport
+x = {}  # Amount of service allocated
+y = {}  # Binary variable indicating provider selection
+w = {}  # Egress costs between providers
+
+# Create decision variables
+for i, provider_from in enumerate(['AWS', 'Azure', 'Google']):
+    for j, service in enumerate(['C1', 'C2', 'AI/ML']):
+        x[i, j] = model.addVar(vtype=GRB.CONTINUOUS, lb=0, name=f"{provider_from}_{service}")
+        y[i, j] = model.addVar(vtype=GRB.BINARY, name=f"provider_{provider_from}_service_{service}")
+        for k, provider_to in enumerate(['AWS', 'Azure', 'Google']):
+            if provider_from != provider_to:
+                w[i, j, k] = model.addVar(vtype=GRB.CONTINUOUS, lb=0, name=f"egress_{provider_from}_{service}_{provider_to}")
+
+# Update model
+model.update()
+
+# Objective function: Minimize total cost (including egress costs)
+total_cost = (
+    sum(costs_AWS[service] * x[0, j] for j, service in enumerate(['C1', 'C2', 'AI/ML'])) +
+    sum(costs_Azure[service] * x[1, j] for j, service in enumerate(['C1', 'C2', 'AI/ML'])) +
+    sum(costs_Google[service] * x[2, j] for j, service in enumerate(['C1', 'C2', 'AI/ML'])) +
+    sum(inter_provider_egress[('AWS', 'Azure')] * w[0, j, 1] for j in range(n)) +
+    sum(inter_provider_egress[('AWS', 'Google')] * w[0, j, 2] for j in range(n)) +
+    sum(inter_provider_egress[('Azure', 'Google')] * w[1, j, 2] for j in range(n)) +
+    sum(inter_provider_egress[('Azure', 'AWS')] * w[1, j, 0] for j in range(n)) +
+    sum(inter_provider_egress[('Google', 'AWS')] * w[2, j, 0] for j in range(n)) +
+    sum(inter_provider_egress[('Google', 'Azure')] * w[2, j, 1] for j in range(n))
+)
+model.setObjective(total_cost, GRB.MINIMIZE)
+
+# Supply constraints
+for i, provider in enumerate(['AWS', 'Azure', 'Google']):
+    for j, service in enumerate(['C1', 'C2', 'AI/ML']):
+        supply_limit = {'AWS': supply_AWS, 'Azure': supply_Azure, 'Google': supply_Google}[provider][service]
+        model.addConstr(x[i, j] <= supply_limit * y[i, j], name=f"supply_{provider}_{service}")
+
+# Demand constraints
+for j, service in enumerate(['C1', 'C2', 'AI/ML']):
+    model.addConstr(sum(x[i, j] for i in range(m)) == demand[service], name=f"demand_{service}")
+
+# Budget constraint
+model.addConstr(total_cost <= budget, name="BudgetConstraint")
+
+# Only one provider per service
+for j, service in enumerate(['C1', 'C2', 'AI/ML']):
+    model.addConstr(sum(y[i, j] for i in range(m)) == 1, name=f"one_provider_per_service_{service}")
+
+# Egress constraints: Egress cost applies only when transferring between providers
+for i in range(m):
+    for j in range(n):
+        for k in range(m):
+            if i != k:
+                model.addConstr(w[i, j, k] <= x[i, j], name=f"egress_constraint_{i}_{j}_{k}")
+
+# Optimize model
+model.optimize()
+
+# Display results in a table format if optimal solution is found
+if model.status == GRB.OPTIMAL:
+    # Create a list to hold the results
+    results = []
+
+    # Calculate egress costs dynamically for each provider
+    egress_costs = {'AWS': 0, 'Azure': 0, 'Google': 0}
+    for i, provider_from in enumerate(['AWS', 'Azure', 'Google']):
+        for j in range(n):
+            for k, provider_to in enumerate(['AWS', 'Azure', 'Google']):
+                if provider_from != provider_to:
+                    egress_costs[provider_from] += w[i, j, k].x * inter_provider_egress[(provider_from, provider_to)]
+
+    # Gather results for each provider
+    for i, provider in enumerate(['AWS', 'Azure', 'Google']):
+        row = [provider]
+        # Add allocated quantities of each service
+        for j, service in enumerate(['C1', 'C2', 'AI/ML']):
+            allocated_quantity = x[i, j].x
+            row.append(allocated_quantity)
+        # Append the calculated egress cost
+        row.append(egress_costs[provider])
+        results.append(row)
+
+    # Convert to DataFrame for display
+    columns = ['Provider', 'C1 (units)', 'C2 (units)', 'AI/ML (units)', 'Egress Cost ($)']
+    df = pd.DataFrame(results, columns=columns)
+
+    # Display the DataFrame and the total optimal cost
+    print(f"Optimal Cost: {model.objVal}")
+    print(df)
+else:
+    print("No optimal solution found")
+