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159 lines
4.5 KiB
Python
159 lines
4.5 KiB
Python
import random
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import time
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import numpy as np
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from coapthon import defines
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class SuperviseurLocalPlaceHolder():
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"""Class de base pour le superviseur
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"""
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def __init__(self, client_CoAP) -> None:
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client_CoAP.superviseur = self
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self.client = client_CoAP
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self._RTTs = []
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self._taux_retransmition = 0
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self._RTO = defines.ACK_TIMEOUT
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def envoie_message(self, message) -> None:
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self.envoie_token(message.token)
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def reception_message(self, message) -> None:
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self.reception_token(message.token)
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def envoie_token(self, token) -> None:
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pass
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def reception_token(self, tokken) -> None:
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pass
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@property
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def RTTs(self):
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return self._RTTs
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@property
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def taux_retransmission(self):
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return self._taux_retransmition
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@property
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def min_RTT(self):
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"""Valeur minimum du RTT"""
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return min(self.RTTs)
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@property
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def avg_RTT(self):
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"""Moyenne du RTT."""
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return sum(self.RTTs)/len(self.RTTs)
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@property
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def RTO(self):
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return random.uniform(self._RTO, (self._RTO * defines.ACK_RANDOM_FACTOR))
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class SuperviseurLocal(SuperviseurLocalPlaceHolder):
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"""
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Class implementant la supervision local de chaque client.
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"""
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def __init__(self, client_CoAP) -> None:
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super().__init__(client_CoAP)
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self._dict_envoie = {}
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self._n_envoie = 0
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self._n_tokken = 0
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def envoie_token(self, token) -> None:
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"""Enregistre l'envoie d'un token
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Args:
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token (int): Token à enregistrer
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"""
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self._n_envoie += 1
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self._n_tokken += not(token in self._dict_envoie)
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self._dict_envoie[token] = time.time()
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self._taux_retransmition = 1 - self._n_tokken/self._n_envoie
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def reception_token(self, token) -> None:
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"""Enregistre l'arrivée d'un token
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Args:
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token (int): Token à enregister
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"""
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if token in self._dict_envoie:
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rtt = time.time() - self._dict_envoie[token]
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self._RTTs.append(time.time() - self._dict_envoie[token])
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# del self._dict_envoie[token]
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self.callback_new_rtt(rtt)
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else:
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pass # raise ValueError("Tokken inconnue")
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def callback_new_rtt(self, rtt):
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pass
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def reset(self):
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self._dict_envoie = {}
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self._n_envoie = 0
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self._n_tokken = 0
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self._RTTs = []
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class SuperviseurLocalFiltre(SuperviseurLocal):
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def __init__(self, client_CoAP, rtt_init=0.01, alpha_l=0.01, alpha_s=0.1) -> None:
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super().__init__(client_CoAP)
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self.alpha_l = alpha_l
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self.alpha_s = alpha_s
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self._RTT_L = rtt_init
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self._RTT_S = rtt_init
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def callback_new_rtt(self, rtt):
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self._RTT_L = rtt*self.alpha_l + (1 - self.alpha_l) * self._RTT_L
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self._RTT_S = rtt*self.alpha_s + (1 - self.alpha_s) * self._RTT_S
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return super().callback_new_rtt(rtt)
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@property
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def RTT_L(self):
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return self._RTT_L
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@property
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def RTT_S(self):
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return self._RTT_S
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class SuperviseurGlobal():
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def __init__(self, clients, superviseur_type, *superviseur_args) -> None:
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"""Genère un superviseur global pour la liste de client donnée
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Args:
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clients (List(HelperClient)): Liste des clients à supervisé
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superviseur_type (Type): Type de superviseur à utilisé
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"""
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self.clients = clients
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self.superviseurs = [superviseur_type(
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client, *superviseur_args) for client in clients]
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@property
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def state(self):
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taux_retransmissions = np.array(
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[superviseur.taux_retransmission for superviseur in self.superviseurs])
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min_rtts = np.array(
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[superviseur.min_RTT for superviseur in self.superviseurs])
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avg_rtts = np.array(
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[superviseur.avg_RTT for superviseur in self.superviseurs])
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ratio_rtts = np.array(min_rtts/avg_rtts)
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if isinstance(self.superviseurs[0], SuperviseurLocalFiltre):
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rtt_ls = np.array(
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[superviseur.RTT_L for superviseur in self.superviseurs])
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rtt_ss = np.array(
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[superviseur.RTT_S for superviseur in self.superviseurs])
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ratio_filtres = rtt_ss/rtt_ls
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representation_etat = np.array(
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[taux_retransmissions, ratio_rtts, ratio_filtres])
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else:
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representation_etat = np.array([taux_retransmissions, ratio_rtts])
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return representation_etat
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