diff --git a/extraction_caracteristiques_utiles.py b/extraction_caracteristiques_utiles.py
index ecde13524ec88e001f17961d1565b0c15e1f1ded..17a4cb4a268b246e359d9251f3f30edb327ff8f7 100644
--- a/extraction_caracteristiques_utiles.py
+++ b/extraction_caracteristiques_utiles.py
@@ -14,7 +14,7 @@ def parse_server_usage(filepath):
- cpu_usage (colonne 2)
- mem_usage (colonne 3)
"""
- df = pd.read_csv(filepath, header=None)
+ df = pd.read_csv(filepath, header=None, nrows=100)
df = df[[0, 1, 2, 3]]
df.columns = ['timestamp', 'machine_id', 'cpu_usage', 'mem_usage']
return df.to_dict(orient='records')
@@ -27,7 +27,7 @@ def parse_container_usage(filepath):
- cpu_usage (colonne 2)
- mem_usage (colonne 3)
"""
- df = pd.read_csv(filepath, header=None)
+ df = pd.read_csv(filepath, header=None, nrows=100)
df = df[[0, 1, 2, 3]]
df.columns = ['timestamp', 'container_id', 'cpu_usage', 'mem_usage']
return df.to_dict(orient='records')
@@ -40,7 +40,7 @@ def parse_server_event(filepath):
- event_type (colonne 2)
- capacity (colonne 4)
"""
- df = pd.read_csv(filepath, header=None)
+ df = pd.read_csv(filepath, header=None, nrows=100)
df = df[[0, 1, 2, 4]]
df.columns = ['timestamp', 'machine_id', 'event_type', 'capacity']
return df.to_dict(orient='records')
@@ -53,7 +53,7 @@ def parse_container_event(filepath):
- container_id (colonne 2)
- machine_list (colonne 7) qui est transformée en liste d'entiers
"""
- df = pd.read_csv(filepath, header=None)
+ df = pd.read_csv(filepath, header=None, nrows=100)
df = df[[0, 1, 2, 7]]
df.columns = ['timestamp', 'event_type', 'container_id', 'machine_list']
@@ -76,7 +76,7 @@ def parse_batch_instance(filepath):
- instance_id (colonne 4)
- status (colonne 5)
"""
- df = pd.read_csv(filepath, header=None)
+ df = pd.read_csv(filepath, header=None, nrows=100)
df = df[[0, 1, 2, 3, 4, 5]]
df.columns = ['start_time', 'end_time', 'cpu_alloc', 'mem_alloc', 'instance_id', 'status']
return df.to_dict(orient='records')
@@ -93,7 +93,7 @@ def parse_batch_task(filepath):
- resource_weight (colonne 6)
- other_metric (colonne 7)
"""
- df = pd.read_csv(filepath, header=None)
+ df = pd.read_csv(filepath, header=None, nrows=100)
df = df[[0, 1, 2, 3, 4, 5, 6, 7]]
df.columns = ['start_time', 'end_time', 'num_instances', 'priority', 'job_id', 'status', 'resource_weight', 'other_metric']
return df.to_dict(orient='records')
diff --git a/run_simulation.py b/run_simulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..afe7989c5cee17b0fbe0fcff080520b77e1d9d39
--- /dev/null
+++ b/run_simulation.py
@@ -0,0 +1,284 @@
+import simpy
+import json
+import os
+
+# =============================================================================
+# Définition des classes de simulation
+# =============================================================================
+
+class Machine:
+ def __init__(self, env, machine_id, cpu_capacity, mem_capacity, p_idle, p_peak, alpha, overbooking_threshold=1.1):
+ """
+ env: environnement SimPy
+ machine_id: identifiant de la machine
+ cpu_capacity, mem_capacity: capacités de la machine
+ p_idle: puissance consommée à l'état inactif
+ p_peak: puissance consommée au pic (100% charge CPU)
+ alpha: coefficient pour la charge mémoire
+ overbooking_threshold: seuil pour autoriser le surbooking (ex: 1.1 pour 10% de surbooking)
+ """
+ self.env = env
+ self.machine_id = machine_id
+ self.cpu_capacity = cpu_capacity
+ self.mem_capacity = mem_capacity
+ self.p_idle = p_idle
+ self.p_peak = p_peak
+ self.alpha = alpha
+ self.overbooking_threshold = overbooking_threshold
+
+ # Utilisation courante
+ self.current_cpu_usage = 0.0
+ self.current_mem_usage = 0.0
+
+ self.energy_consumption = 0.0 # énergie cumulée
+ self.running_tasks = [] # liste des tâches en cours sur la machine
+
+ def can_allocate(self, cpu_req, mem_req):
+ """
+ Vérifie si la machine peut accepter la demande en tenant compte du surbooking.
+ """
+ if (self.current_cpu_usage + cpu_req <= self.overbooking_threshold * self.cpu_capacity and
+ self.current_mem_usage + mem_req <= self.overbooking_threshold * self.mem_capacity):
+ return True
+ return False
+
+ def allocate(self, task):
+ """
+ Alloue les ressources pour la tâche et l’ajoute à la liste des tâches actives.
+ """
+ self.current_cpu_usage += task.cpu_req
+ self.current_mem_usage += task.mem_req
+ self.running_tasks.append(task)
+ print(f"[{self.env.now:.2f}] Tâche {task.id} assignée à la machine {self.machine_id}")
+
+ def release(self, task):
+ """
+ Libère les ressources occupées par la tâche.
+ """
+ self.current_cpu_usage -= task.cpu_req
+ self.current_mem_usage -= task.mem_req
+ if task in self.running_tasks:
+ self.running_tasks.remove(task)
+ print(f"[{self.env.now:.2f}] Tâche {task.id} terminée sur la machine {self.machine_id}")
+
+ def compute_power(self):
+ """
+ Calcule la puissance instantanée selon un modèle linéaire:
+ P = P_idle + (P_peak - P_idle)* (current_cpu/cpu_capacity) + alpha*(current_mem/mem_capacity)
+ """
+ cpu_util = self.current_cpu_usage / self.cpu_capacity if self.cpu_capacity > 0 else 0
+ mem_util = self.current_mem_usage / self.mem_capacity if self.mem_capacity > 0 else 0
+ power = self.p_idle + (self.p_peak - self.p_idle) * cpu_util + self.alpha * mem_util
+ return power
+
+ def run(self):
+ """
+ Processus SimPy qui, à chaque pas de temps, met à jour la consommation énergétique.
+ """
+ while True:
+ power = self.compute_power()
+ dt = 1 # pas de temps (peut être affiné)
+ self.energy_consumption += power * dt
+ yield self.env.timeout(dt)
+
+# -----------------------------------------------------------------------------
+# Pour simplifier, on utilise la même classe pour représenter une tâche
+# (batch ou container). On pourra par la suite la spécialiser.
+# -----------------------------------------------------------------------------
+class Task:
+ def __init__(self, id, arrival_time, duration, cpu_req, mem_req, task_type="batch"):
+ """
+ id: identifiant (peut être job_id ou container_id)
+ arrival_time: moment d'arrivée dans la simulation
+ duration: durée d'exécution de la tâche
+ cpu_req, mem_req: ressources demandées
+ task_type: 'batch' ou 'container'
+ """
+ self.id = id
+ self.arrival_time = arrival_time
+ self.duration = duration
+ self.cpu_req = cpu_req
+ self.mem_req = mem_req
+ self.task_type = task_type
+
+# -----------------------------------------------------------------------------
+# Ordonnanceur simple (First-Fit)
+# -----------------------------------------------------------------------------
+class Scheduler:
+ def __init__(self, env, machines):
+ self.env = env
+ self.machines = machines
+ self.rejected_tasks = 0
+
+ def schedule_task(self, task):
+ """
+ Affecte la tâche à la première machine capable d'allouer les ressources.
+ En cas d'échec, la tâche est rejetée.
+ """
+ for machine in self.machines:
+ if machine.can_allocate(task.cpu_req, task.mem_req):
+ machine.allocate(task)
+ self.env.process(self.run_task(machine, task))
+ return True
+ print(f"[{self.env.now:.2f}] Tâche {task.id} rejetée (aucune machine disponible)")
+ self.rejected_tasks += 1
+ return False
+
+ def run_task(self, machine, task):
+ """
+ Processus simulant l'exécution de la tâche.
+ À la fin, les ressources sont libérées.
+ """
+ yield self.env.timeout(task.duration)
+ machine.release(task)
+
+# =============================================================================
+# Fonctions d'extraction des machines et des workloads à partir des paramètres sauvegardés
+# =============================================================================
+
+def load_simulation_parameters(json_file):
+ """
+ Charge le fichier JSON contenant les paramètres filtrés issus des traces Alibaba.
+ """
+ if not os.path.exists(json_file):
+ raise FileNotFoundError(f"{json_file} introuvable.")
+ with open(json_file, 'r') as f:
+ params = json.load(f)
+ return params
+
+def extract_machines(env, params):
+ """
+ Extrait les machines à partir des événements serveurs (server_event).
+ On considère uniquement les événements 'add'.
+ Pour cet exemple, on définit :
+ - cpu_capacity = capacity (colonne 'capacity')
+ - mem_capacity = 2 * capacity (valeur arbitraire)
+ - Paramètres énergétiques fixes (p_idle, p_peak, alpha)
+ """
+ machines = {}
+ for rec in params.get("server_event", []):
+ event_type = rec.get("event_type", "").strip().lower()
+ if event_type == "add":
+ machine_id = rec["machine_id"]
+ capacity = rec["capacity"]
+ # On crée la machine si elle n'existe pas déjà
+ if machine_id not in machines:
+ machines[machine_id] = Machine(
+ env=env,
+ machine_id=machine_id,
+ cpu_capacity=int(capacity),
+ mem_capacity=int(capacity) * 2, # exemple de définition
+ p_idle=50,
+ p_peak=200,
+ alpha=10,
+ overbooking_threshold=1.1
+ )
+ return list(machines.values())
+
+def extract_batch_tasks(params):
+ """
+ Extrait les tâches batch à partir de 'batch_task'.
+ On définit :
+ - arrival_time = start_time
+ - duration = end_time - start_time
+ - cpu_req basé sur resource_weight (scalé) et mem_req fixé.
+ """
+ tasks = []
+ for rec in params.get("batch_task", []):
+ start_time = float(rec["start_time"])
+ end_time = float(rec["end_time"])
+ duration = end_time - start_time
+
+ if duration <= 0:
+ print(f"⚠️ Durée négative ou nulle pour la tâche {rec['job_id']} : start={start_time}, end={end_time}")
+ continue
+
+ cpu_req = float(rec["resource_weight"]) / 10.0 # échelle arbitraire
+ mem_req = 1.0 # valeur par défaut
+ task_id = rec["job_id"]
+ tasks.append(Task(task_id, start_time, duration, cpu_req, mem_req, task_type="batch"))
+ return tasks
+
+def extract_containers(params):
+ """
+ Extrait les containers à partir de 'container_usage'.
+ On définit :
+ - arrival_time = timestamp
+ - duration : ici fixé arbitrairement (ex: 100 unités de temps)
+ - cpu_req et mem_req sont basés sur les valeurs observées (scalées)
+ """
+ containers = []
+ for rec in params.get("container_usage", []):
+ arrival_time = float(rec["timestamp"])
+ container_id = rec["container_id"]
+ cpu_req = float(rec["cpu_usage"]) / 10.0 # échelle arbitraire
+ mem_req = float(rec["mem_usage"]) / 10.0 # échelle arbitraire
+ duration = 100.0 # durée fixe pour la simulation (peut être adapté)
+ containers.append(Task(container_id, arrival_time, duration, cpu_req, mem_req, task_type="container"))
+ return containers
+
+# =============================================================================
+# Processus d'arrivée des tâches / containers
+# =============================================================================
+
+def arrival_generator(env, tasks, scheduler):
+ """
+ Parcourt la liste des tâches (batch et containers) triée par temps d'arrivée et
+ lance leur envoi vers l'ordonnanceur.
+ """
+ # Tri par temps d'arrivée
+ tasks_sorted = sorted(tasks, key=lambda t: t.arrival_time)
+ for task in tasks_sorted:
+ # Attendre jusqu'à l'arrivée de la tâche
+ if env.now < task.arrival_time:
+ yield env.timeout(task.arrival_time - env.now)
+ print(f"[{env.now:.2f}] Arrivée de la tâche {task.id} ({task.task_type})")
+ scheduler.schedule_task(task)
+
+# =============================================================================
+# Fonction principale de simulation
+# =============================================================================
+
+def run_simulation(json_file):
+ # Charger les paramètres sauvegardés
+ params = load_simulation_parameters(json_file)
+
+ # Créer l'environnement SimPy
+ env = simpy.Environment()
+
+ # Extraire et créer les machines
+ machines = extract_machines(env, params)
+ for m in machines:
+ env.process(m.run()) # lancement du processus de suivi énergétique
+
+ # Extraire les workloads
+ batch_tasks = extract_batch_tasks(params)
+ containers = extract_containers(params)
+
+ # Combine les deux types de workloads
+ all_tasks = batch_tasks + containers
+
+ # Créer l'ordonnanceur (First-Fit)
+ scheduler = Scheduler(env, machines)
+
+ # Lancer le générateur d'arrivées
+ env.process(arrival_generator(env, all_tasks, scheduler))
+
+ # Déterminer une durée de simulation suffisante (ici, on se base sur la dernière arrivée + durée)
+ sim_time = max(t.arrival_time + t.duration for t in all_tasks) + 10
+ env.run(until=sim_time)
+
+ # Affichage des métriques
+ total_energy = sum(m.energy_consumption for m in machines)
+ print("\n--- Résultats de la simulation ---")
+ print(f"Durée totale : {sim_time} unités de temps")
+ print(f"Énergie totale consommée : {total_energy:.2f}")
+ print(f"Tâches rejetées : {scheduler.rejected_tasks}")
+
+# =============================================================================
+# Point d'entrée
+# =============================================================================
+
+if __name__ == '__main__':
+ json_file = 'simulation_parameters.json'
+ run_simulation(json_file)
diff --git a/simulation_parameters.json b/simulation_parameters.json
new file mode 100644
index 0000000000000000000000000000000000000000..e98a5db3ef3db2dc6228cd4ecd3fc1f65bfac070
--- /dev/null
+++ b/simulation_parameters.json
@@ -0,0 +1,4916 @@
+{
+ "server_usage": [
+ {
+ "timestamp": 41700,
+ "machine_id": 237,
+ "cpu_usage": 23.3799999237,
+ "mem_usage": 30.08000030518
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 265,
+ "cpu_usage": 26.3599998474,
+ "mem_usage": 29.53999977114
+ },
+ {
+ "timestamp": 42600,
+ "machine_id": 770,
+ "cpu_usage": 49.14000015258,
+ "mem_usage": 60.09999923706
+ },
+ {
+ "timestamp": 40800,
+ "machine_id": 776,
+ "cpu_usage": 33.23999977112,
+ "mem_usage": 47.52000045776
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 393,
+ "cpu_usage": 45.72000045776,
+ "mem_usage": 58.72000045778
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 610,
+ "cpu_usage": 41.7,
+ "mem_usage": 59.22000122072001
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 719,
+ "cpu_usage": 43.57999954222,
+ "mem_usage": 63.30000076294
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 723,
+ "cpu_usage": 42.48000030518,
+ "mem_usage": 66.2
+ },
+ {
+ "timestamp": 40200,
+ "machine_id": 742,
+ "cpu_usage": 35.90000000002,
+ "mem_usage": 57.70000076294
+ },
+ {
+ "timestamp": 40200,
+ "machine_id": 22,
+ "cpu_usage": 45.7,
+ "mem_usage": 61.8800010681
+ },
+ {
+ "timestamp": 40800,
+ "machine_id": 47,
+ "cpu_usage": 41.22000007632,
+ "mem_usage": 50.24000015256
+ },
+ {
+ "timestamp": 41100,
+ "machine_id": 109,
+ "cpu_usage": 27.339999771100004,
+ "mem_usage": 45.92000045778
+ },
+ {
+ "timestamp": 39900,
+ "machine_id": 866,
+ "cpu_usage": 32.6799999237,
+ "mem_usage": 52.12000045776
+ },
+ {
+ "timestamp": 42300,
+ "machine_id": 884,
+ "cpu_usage": 45.89999923706,
+ "mem_usage": 58.21999969482
+ },
+ {
+ "timestamp": 41400,
+ "machine_id": 903,
+ "cpu_usage": 55.53999938966,
+ "mem_usage": 53.88000030520001
+ },
+ {
+ "timestamp": 42600,
+ "machine_id": 1221,
+ "cpu_usage": 39.06000061036,
+ "mem_usage": 53.16000061034
+ },
+ {
+ "timestamp": 39900,
+ "machine_id": 1285,
+ "cpu_usage": 42.44000091554,
+ "mem_usage": 71.58000030518
+ },
+ {
+ "timestamp": 40500,
+ "machine_id": 1068,
+ "cpu_usage": 25.46000022888,
+ "mem_usage": 49.12000045776
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 1023,
+ "cpu_usage": 33.68000030518,
+ "mem_usage": 52.53999938966
+ },
+ {
+ "timestamp": 42300,
+ "machine_id": 1011,
+ "cpu_usage": 37.95999984742,
+ "mem_usage": 53.64000015258
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 1105,
+ "cpu_usage": 26.820000457780004,
+ "mem_usage": 31.68000030518
+ },
+ {
+ "timestamp": 41100,
+ "machine_id": 477,
+ "cpu_usage": 18.839999771100004,
+ "mem_usage": 26.18000030514
+ },
+ {
+ "timestamp": 41100,
+ "machine_id": 569,
+ "cpu_usage": 34.39999923708,
+ "mem_usage": 62.69999923704
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 574,
+ "cpu_usage": 39.36000061034,
+ "mem_usage": 59.75999984742
+ },
+ {
+ "timestamp": 41100,
+ "machine_id": 342,
+ "cpu_usage": 26.9200000763,
+ "mem_usage": 54.7199996948
+ },
+ {
+ "timestamp": 41400,
+ "machine_id": 1032,
+ "cpu_usage": 41.71999969482,
+ "mem_usage": 56.11999969484
+ },
+ {
+ "timestamp": 40500,
+ "machine_id": 783,
+ "cpu_usage": 46.77999954222,
+ "mem_usage": 59.70000076294
+ },
+ {
+ "timestamp": 40200,
+ "machine_id": 800,
+ "cpu_usage": 8.080000019076,
+ "mem_usage": 35.98000030518
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 664,
+ "cpu_usage": 33.70000076294,
+ "mem_usage": 56.03999938961999
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 679,
+ "cpu_usage": 52.34000015258,
+ "mem_usage": 90.74000091554
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 681,
+ "cpu_usage": 40.61999969482,
+ "mem_usage": 60.36000061036
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 695,
+ "cpu_usage": 66.7,
+ "mem_usage": 76.18000030518
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 1,
+ "cpu_usage": 34.8,
+ "mem_usage": 55.23999938966
+ },
+ {
+ "timestamp": 42300,
+ "machine_id": 67,
+ "cpu_usage": 35.24000015258,
+ "mem_usage": 43.89999999998
+ },
+ {
+ "timestamp": 40800,
+ "machine_id": 84,
+ "cpu_usage": 33.32000045776,
+ "mem_usage": 55.87999954224
+ },
+ {
+ "timestamp": 41400,
+ "machine_id": 901,
+ "cpu_usage": 59.13999938964,
+ "mem_usage": 55.78000030518
+ },
+ {
+ "timestamp": 42000,
+ "machine_id": 929,
+ "cpu_usage": 34.14000015258,
+ "mem_usage": 47.80000000002
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 434,
+ "cpu_usage": 25.5200000763,
+ "mem_usage": 36.7400001526
+ },
+ {
+ "timestamp": 40800,
+ "machine_id": 438,
+ "cpu_usage": 14.2799999237,
+ "mem_usage": 32.65999984742
+ },
+ {
+ "timestamp": 40800,
+ "machine_id": 439,
+ "cpu_usage": 16.49999980928,
+ "mem_usage": 32.179999160760005
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 471,
+ "cpu_usage": 25.91999969482,
+ "mem_usage": 35.78000030518
+ },
+ {
+ "timestamp": 40500,
+ "machine_id": 530,
+ "cpu_usage": 19.72000007628,
+ "mem_usage": 27.88000030518
+ },
+ {
+ "timestamp": 39900,
+ "machine_id": 135,
+ "cpu_usage": 25.56000022886,
+ "mem_usage": 32.75999984742
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 555,
+ "cpu_usage": 37.25999984742,
+ "mem_usage": 63.51999969482
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 198,
+ "cpu_usage": 35.56000061034,
+ "mem_usage": 56.36000061036
+ },
+ {
+ "timestamp": 41400,
+ "machine_id": 1136,
+ "cpu_usage": 34.72000045774,
+ "mem_usage": 58.19999923708
+ },
+ {
+ "timestamp": 42300,
+ "machine_id": 1146,
+ "cpu_usage": 37.68000030518,
+ "mem_usage": 58.18000030516
+ },
+ {
+ "timestamp": 40200,
+ "machine_id": 1244,
+ "cpu_usage": 34.5599998474,
+ "mem_usage": 57.05999908445999
+ },
+ {
+ "timestamp": 39900,
+ "machine_id": 219,
+ "cpu_usage": 30.24000015258,
+ "mem_usage": 57.81999969482
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 229,
+ "cpu_usage": 47.12000045776,
+ "mem_usage": 57.45999984742
+ },
+ {
+ "timestamp": 42300,
+ "machine_id": 259,
+ "cpu_usage": 28.7799999237,
+ "mem_usage": 49.60000076292
+ },
+ {
+ "timestamp": 40200,
+ "machine_id": 1287,
+ "cpu_usage": 29.08000030516,
+ "mem_usage": 49.68000030518
+ },
+ {
+ "timestamp": 41700,
+ "machine_id": 697,
+ "cpu_usage": 42.34000015258,
+ "mem_usage": 55.91999893187999
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 1163,
+ "cpu_usage": 42.5,
+ "mem_usage": 55.8800003052
+ },
+ {
+ "timestamp": 42300,
+ "machine_id": 1151,
+ "cpu_usage": 43.5400001526,
+ "mem_usage": 66.7
+ },
+ {
+ "timestamp": 40200,
+ "machine_id": 417,
+ "cpu_usage": 33.27999954224,
+ "mem_usage": 52.37999954224
+ },
+ {
+ "timestamp": 40500,
+ "machine_id": 25,
+ "cpu_usage": 40.93999938963999,
+ "mem_usage": 58.20000000002
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 85,
+ "cpu_usage": 39.7,
+ "mem_usage": 54.11999969482
+ },
+ {
+ "timestamp": 42900,
+ "machine_id": 115,
+ "cpu_usage": 35.1,
+ "mem_usage": 51.07999954224
+ },
+ {
+ "timestamp": 39600,
+ "machine_id": 843,
+ "cpu_usage": 43.7199996948,
+ "mem_usage": 54.90000000002
+ },
+ {
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\ No newline at end of file