simulation start with the vent generator

Author: GioeleB00

src/app/api/simulation.py

@@ -3,33 +3,16 @@
 import numpy as np
 from fastapi import APIRouter
 
-from app.core.requests_generator import requests_generator
+from app.core.simulation.simulator import run_simulation
 from app.schemas.simulation_input import SimulationInput
 from app.schemas.simulation_output import SimulationOutput
 
 router = APIRouter()
 
 @router.post("/simulation")
-async def event_loop_simulation(
-    generator_input: SimulationInput,
-) -> SimulationOutput:
-    """Endpoint to handle the simulation."""
-    # Define the variables for the generator
-    # Implement a robust type checking for
-    # edge cases
-    rng: np.random.Generator = np.random.default_rng()
-
-    # requests generator
-    req_generated  = requests_generator( # noqa: F841
-        generator_input,
-        rng=rng,
-    )
-
-    # simple map to pass the quality assurance
-    return SimulationOutput(
-        metric_1=generator_input.avg_active_users.mean,
-        metric_2=str(generator_input.avg_request_per_minute_per_user.mean),
-        metric_n=str(generator_input.total_simulation_time),
-    )
+async def event_loop_simulation(input_data: SimulationInput) -> SimulationOutput:
+    """Run the simulation and return aggregate KPIs."""
+    rng = np.random.default_rng()
+    return run_simulation(input_data, rng=rng)
 
 

src/app/core/event_samplers/gaussian_poisson.py

@@ -36,15 +36,15 @@ def gaussian_poisson_sampling(
          Λ = U * (mean_req_per_minute_per_user / 60)  [req/s].
     3. While inside the current window, draw gaps
          Δt ~ Exponential(Λ)   using inverse-CDF.
-    4. Stop once the virtual clock exceeds *simulation_time_second*.
+    4. Stop once the virtual clock exceeds *simulation_time*.
     """
     rng = rng or np.random.default_rng()
 
-    simulation_time_second = input_data.total_simulation_time
+    simulation_time = input_data.total_simulation_time
     # pydantic in the validation assign a value and mypy is not
     # complaining because a None cannot be compared in the loop
     # to a float
-    assert simulation_time_second is not None
+    assert simulation_time is not None
 
     # λ_u : mean concurrent users per window
     mean_concurrent_user = float(input_data.avg_active_users.mean)
@@ -65,7 +65,7 @@ def gaussian_poisson_sampling(
     window_end = 0.0          # end of the current user window
     lam = 0.0                 # aggregate rate Λ (req/s)
 
-    while now < simulation_time_second:
+    while now < simulation_time:
         # (Re)sample U at the start of each window
         if now >= window_end:
             window_end = now + float(sampling_window_s)
@@ -86,7 +86,7 @@ def gaussian_poisson_sampling(
         delta_t = -math.log(1.0 - u_raw) / lam
 
         # End simulation if the next event exceeds the horizon
-        if now + delta_t > simulation_time_second:
+        if now + delta_t > simulation_time:
             break
 
         # If the gap crosses the window boundary, jump to it

src/app/core/event_samplers/poisson_poisson.py

@@ -33,15 +33,15 @@ def poisson_poisson_sampling(
          Λ = U * (mean_req_per_minute_per_user / 60)  [req/s].
     3. While inside the current window, draw gaps
          Δt ~ Exponential(Λ)   using inverse-CDF.
-    4. Stop once the virtual clock exceeds *simulation_time_second*.
+    4. Stop once the virtual clock exceeds *simulation_time*.
     """
     rng = rng or np.random.default_rng()
 
-    simulation_time_second = input_data.total_simulation_time
+    simulation_time = input_data.total_simulation_time
     # pydantic in the validation assign a value and mypy is not
     # complaining because a None cannot be compared in the loop
     # to a float
-    assert simulation_time_second is not None
+    assert simulation_time is not None
 
     # λ_u : mean concurrent users per window
     mean_concurrent_user = float(input_data.avg_active_users.mean)
@@ -57,7 +57,7 @@ def poisson_poisson_sampling(
     window_end = 0.0          # end of the current user window
     lam = 0.0                 # aggregate rate Λ (req/s)
 
-    while now < simulation_time_second:
+    while now < simulation_time:
         # (Re)sample U at the start of each window
         if now >= window_end:
             window_end = now + float(sampling_window_s)
@@ -74,7 +74,7 @@ def poisson_poisson_sampling(
         delta_t = -math.log(1.0 - u_raw) / lam
 
         # End simulation if the next event exceeds the horizon
-        if now + delta_t > simulation_time_second:
+        if now + delta_t > simulation_time:
             break
 
         # If the gap crosses the window boundary, jump to it

src/app/core/simulation/simulator.py

@@ -0,0 +1,59 @@
+"""simulation of the server"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import simpy
+
+from app.core.requests_generator import requests_generator
+from app.schemas.simulation_output import SimulationOutput
+
+if TYPE_CHECKING:
+    from collections.abc import Generator, Iterator
+
+    import numpy as np
+
+    from app.schemas.simulation_input import SimulationInput
+
+
+
+
+def run_simulation(
+    data: SimulationInput,
+    *,
+    rng: np.random.Generator,
+) -> SimulationOutput:
+    """
+    Esegue la simulazione in SimPy usando i gap prodotti da requests_generator.
+    Per ora calcola solo il numero totale di richieste arrivate.
+    """
+    gaps: Iterator[float] = requests_generator(data, rng=rng)
+    env = simpy.Environment()
+
+    simulation_time = data.total_simulation_time
+    # pydantic in the validation assign a value and mypy is not
+    # complaining because a None cannot be compared in the loop
+    # to a float
+    assert simulation_time is not None
+
+    total_request_per_time_period = {
+        "simulation_time": simulation_time,
+        "total_requests": 0,
+        }
+
+    def arrival_process(
+        env: simpy.Environment,
+    ) -> Generator[simpy.events.Event, None, None]:
+        for gap in gaps:
+            yield env.timeout(gap)
+            total_request_per_time_period["total_requests"] += 1
+
+    env.process(arrival_process(env))
+    env.run(until=simulation_time)
+
+    return SimulationOutput(
+        total_requests=total_request_per_time_period,
+        metric_2=str(data.avg_request_per_minute_per_user.mean),
+        metric_n=str(data.avg_active_users.mean),
+    )

src/app/schemas/simulation_output.py

@@ -6,7 +6,7 @@
 class SimulationOutput(BaseModel):
     """Define the output of the simulation"""
 
-    metric_1: str | int
+    total_requests: dict[str, int | float]
     metric_2: str
     #......
     metric_n: str