def bound(self, param_list: List[float]) -> float:
theta = param_list[0]
return evaluate_single_hop(foi=self.arr,
s_net=self.ser,
theta=theta,
perform_param=self.perform_param)
def bound(self, param_list: List[float]) -> float:
theta = param_list[0]
if self.number_servers == 1:
s_net: Service = Leftover(arr=self.arr_list[1],
ser=self.ser_list[0])
elif self.number_servers == 2:
leftover_service_list = [
Leftover(arr=self.arr_list[1], ser=self.ser_list[0]),
AggregateList(arr_list=self.arr_list[2], p_list=[]),
Deconvolve(arr=self.arr_list[1], ser=self.ser_list[0])
]
s_net: Service = Convolve(ser1=leftover_service_list[0],
ser2=leftover_service_list[1],
indep=False,
p=param_list[1])
else:
raise ValueError(
"This number of servers {0} is not implemented yet".format(
self.number_servers))
return evaluate_single_hop(foi=self.arr_list[0],
s_net=s_net,
theta=theta,
perform_param=self.perform_param)
def new_delay(theta: float, delay: int, a_1: ArrivalDistribution,
a_2: ArrivalDistribution, s_1: ConstantRate,
s_2: TokenBucketConstant, s_3: ConstantRate) -> float:
s_net: Service = Convolve(ser1=Leftover(arr=a_2, ser=s_1),
ser2=Leftover(arr=s_2, ser=s_3),
indep=True)
return evaluate_single_hop(foi=a_1,
s_net=s_net,
theta=theta,
perform_param=PerformParameter(
perform_metric=PerformEnum.DELAY_PROB,
value=delay))
def bound(self, param_list: List[float]) -> float:
theta = param_list[0]
s_net: Service = Leftover(arr=self.arr_list[self.number_servers + 1],
ser=self.ser_list[self.number_servers])
for _i in range(self.number_servers, -1, -1):
s_net = Convolve(ser1=s_net, ser2=self.ser_list[_i])
s_net = Leftover(arr=self.arr_list[_i + 1], ser=s_net)
return evaluate_single_hop(foi=self.arr_list[0],
s_net=s_net,
theta=theta,
perform_param=self.perform_param)
def bound(self, param_list: List[float]) -> float:
theta = param_list[0]
leftover_service_list: List[Service] = [
Leftover(arr=self.arr_list[i + 1], ser=self.ser_list[i])
for i in range(self.number_servers)
]
s_net: Service = leftover_service_list[0]
for i in range(1, self.number_servers):
s_net = Convolve(s_net, leftover_service_list[i])
return evaluate_single_hop(
foi=self.arr_list[0],
s_net=s_net,
theta=theta,
perform_param=self.perform_param)
def bound(self, param_list: List[float]) -> float:
theta = param_list[0]
output_list: List[Arrival] = [
Deconvolve(arr=self.arr_list[i], ser=self.ser_list[i])
for i in range(1, self.number_servers)
]
# we use i + 1, since i = 0 is the foi
aggregated_cross: Arrival = AggregateList(
arr_list=output_list, p_list=[])
s_net: Service = Leftover(arr=aggregated_cross, ser=self.ser_list[0])
return evaluate_single_hop(
foi=self.arr_list[0],
s_net=s_net,
theta=theta,
perform_param=self.perform_param)
def standard_delay(theta: float, p: float, delay: int,
a_1: ArrivalDistribution, a_2: ArrivalDistribution,
a_3: ArrivalDistribution, s_1: ConstantRate,
s_2: ConstantRate, s_3: ConstantRate) -> float:
f_3_output: Arrival = Deconvolve(arr=a_3,
ser=Leftover(arr=Deconvolve(arr=a_2,
ser=s_1),
ser=s_2))
s_net: Service = Convolve(ser1=Leftover(arr=a_2, ser=s_1),
ser2=Leftover(arr=f_3_output, ser=s_3),
indep=False,
p=p)
return evaluate_single_hop(foi=a_1,
s_net=s_net,
theta=theta,
perform_param=PerformParameter(
perform_metric=PerformEnum.DELAY_PROB,
value=delay))
def new_bound(self, param_l_list: List[float]) -> float:
# len(param_list) = theta (1) + output bounds (len(arr_list)-1)
if len(param_l_list) != len(self.arr_list):
raise NameError("Check number of parameters")
output_list: List[Arrival] = [
DeconvolvePower(
arr=self.arr_list[i],
ser=self.ser_list[i],
l_power=param_l_list[i])
for i in range(1, self.number_servers)
]
# we use i + 1, since i = 0 is the foi
aggregated_cross: Arrival = AggregateList(
arr_list=output_list, p_list=[])
s_net: Service = Leftover(arr=aggregated_cross, ser=self.ser_list[0])
return evaluate_single_hop(
foi=self.arr_list[0],
s_net=s_net,
theta=param_l_list[0],
perform_param=self.perform_param)