Commit 35314212 authored by Ashkan's avatar Ashkan
Browse files

Backup.

parent ee6ddf8b
from abc import ABC, abstractmethod
import prrt
import math
class AbstractSearch(ABC):
@abstractmethod
def search(self):
pass
class HECFullSearch(AbstractSearch):
def __init__(self, n_p_min, n_p_max, prrtApplicationParameters, prrtChannelParameters, prrtSystemParameters):
self.n_max = 255
self.step_size = 2 # Step size in the estimation of the optimum code word length
self.n_p_min = n_p_min
self.n_p_max = n_p_max
self.prrtApplicationParameters = prrtApplicationParameters
self.prrtChannelParameters = prrtChannelParameters
self.prrtSystemParameters = prrtSystemParameters
self.p_t = self.prrtApplicationParameters.max_residual_loss_rate
self.is_order_ascending = False
pass
def search(self):
ri_opt = math.inf
k_opt = 0
n_opt = 0
n_p_opt = []
# Eq.5.9, page 125
fec_delay_min = self.prrtSystemParameters.source_packet_interval + \
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay + \
(self.prrtChannelParameters.rtt_prop_fwd + self.prrtSystemParameters.processing_delay) / 2 + \
self.prrtSystemParameters.packet_loss_detection_delay
# Eq.5.9 page 125 assumung D_sup = 0
req_delay = self.prrtChannelParameters.rtt_prop_fwd + \
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay + \
self.prrtSystemParameters.processing_delay
# Eq.5.10, page 125
n_c_max = math.ceil((self.prrtApplicationParameters.max_latency - fec_delay_min) / req_delay)
# self.k_lim = somewhat(prrtApplicationParameters.loss_tolerance, self.n_max)
# n_c = 0 is the proactive packet
for n_c in range(n_c_max):
# Eq.5.11, k(Nc, D_T), page 125
k_max = min(self.get_k(n_c, req_delay), self.get_k_lim(1, self.n_max))
for k in range(1, k_max + 1):
n = self.estimate_n_for_k(k)
repair_schedules = prrt.gen_repair_schedule(n - k, n_c, 1, self.n_p_max, self.is_order_ascending)
for repair_schedule in repair_schedules:
coding_conf = prrt.PrrtCodingConfiguration(n, k, repair_schedule)
ri = coding_conf.get_redundant_information(self.prrtChannelParameters)
if ri < ri_opt:
k_opt = k
n_opt = n
n_p_opt = repair_schedule
return prrt.PrrtCodingConfiguration(n_opt, k_opt, n_p_opt)
def get_k(self, n_c, req_delay):
return math.ceil((self.prrtApplicationParameters.max_latency -
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay -
(self.prrtChannelParameters.rtt_prop_fwd + self.prrtSystemParameters.processing_delay) / 2 -
self.prrtSystemParameters.packet_loss_detection_delay -
n_c * req_delay) / self.prrtSystemParameters.source_packet_interval)
def get_k_lim(self, start, end):
mid_point = math.ceil((end - start) / 2)
p_r = self.get_max_coding_block_length(mid_point)
if p_r == self.p_t:
return mid_point
elif p_r > self.p_t:
self.get_k_lim(start, mid_point - 1)
else:
self.get_k_lim(mid_point + 1, end)
# Pr(k, n_max)
def get_max_coding_block_length(self, k):
total_packet_erasure = 0
for i in range(1, k):
for j in range(max(self.n_max - k + 1, i), self.n_max - k + i):
packet_erasure_at_i = i * self.hypergeometric_distribution(self.n_max, k, i, j) * self.get_error_prob(
j, self.n_max, self.prrtChannelParameters.loss_rate_fwd)
total_packet_erasure += packet_erasure_at_i
return (1 / k) * total_packet_erasure
def estimate_n_for_k(self, k):
n = k + 1
while self.residual_packet_erasure_rate(k, n, self.prrtApplicationParameters, self.prrtChannelParameters) > self.prrtApplicationParameters.max_residual_loss_rate\
and n <= self.n_max - self.step_size:
print("")
return 0
def residual_packet_erasure_rate(self, k, n, prrtApplicationParameters, prrtChannelParameters):
total_packet_erasure = 0
for i in range(1, k):
for j in range(max(n - k + 1, i), n - k + i):
packet_erasure_at_i = i * self.hypergeometric_distribution(n, k, i, j) * self.get_error_prob(j, n, prrtChannelParameters.loss_rate_fwd)
total_packet_erasure += packet_erasure_at_i
residual_packet_erasure_rate = (1 / k) * total_packet_erasure # Pr(k, n)
return residual_packet_erasure_rate
class HECReducedSpaceSearch(AbstractSearch):
def __init__(self, n_p_min, n_p_max, prrtApplicationParameters, prrtChannelParameters, prrtSystemParameters):
self.n_max = 255
self.step_size = 2 # Step size in the estimation of the optimum code word length
self.n_p_min = n_p_min
self.n_p_max = n_p_max
self.prrtApplicationParameters = prrtApplicationParameters
self.prrtChannelParameters = prrtChannelParameters
self.prrtSystemParameters = prrtSystemParameters
self.p_t = self.prrtApplicationParameters.max_residual_loss_rate
self.is_order_ascending = True
pass
def search(self):
ri_opt = math.inf
k_opt = 0
n_opt = 0
n_p_opt = []
ric = prrt.RestrictedIntegerComposition
# Eq.5.9, page 125
fec_delay_min = self.prrtSystemParameters.source_packet_interval + \
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay + \
(self.prrtChannelParameters.rtt_prop_fwd + self.prrtSystemParameters.processing_delay) / 2 + \
self.prrtSystemParameters.packet_loss_detection_delay
# Eq.5.9 page 125 assumung D_sup = 0
req_delay = self.prrtChannelParameters.rtt_prop_fwd + \
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay + \
self.prrtSystemParameters.processing_delay
# Eq.5.10, page 125
n_c_max = math.ceil((self.prrtApplicationParameters.max_latency - fec_delay_min) / req_delay)
# self.k_lim = somewhat(prrtApplicationParameters.loss_tolerance, self.n_max)
# n_c = 0 is the proactive packet
for n_c in range(n_c_max):
# Eq.5.11, k(Nc, D_T), page 125
k_max = min(self.get_k(n_c, req_delay), self.get_k_lim(1, self.n_max))
for k in [1, k_max]:
n = self.estimate_n_for_k(k)
repair_schedules = ric.gen_repair_schedule(n - k, n_c, self.n_p_min, self.n_p_max, self.is_order_ascending)
for repair_schedule in repair_schedules:
coding_conf = prrt.PrrtCodingConfiguration(n, k, repair_schedule)
ri = coding_conf.get_redundant_information(self.prrtChannelParameters)
if ri < ri_opt:
k_opt = k
n_opt = n
n_p_opt = repair_schedule
return prrt.PrrtCodingConfiguration(n_opt, k_opt, n_p_opt)
def get_k(self, n_c, req_delay):
return math.ceil((self.prrtApplicationParameters.max_latency -
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay -
(self.prrtChannelParameters.rtt_prop_fwd + self.prrtSystemParameters.processing_delay) / 2 -
self.prrtSystemParameters.packet_loss_detection_delay -
n_c * req_delay) / self.prrtSystemParameters.source_packet_interval)
def get_k_lim(self, start, end):
mid_point = math.ceil((end - start) / 2)
p_r = self.get_max_coding_block_length(mid_point)
if p_r == self.p_t:
return mid_point
elif p_r > self.p_t:
self.get_k_lim(start, mid_point - 1)
else:
self.get_k_lim(mid_point + 1, end)
# Pr(k, n_max)
def get_max_coding_block_length(self, k):
total_packet_erasure = 0
for i in range(1, k):
for j in range(max(self.n_max - k + 1, i), self.n_max - k + i):
packet_erasure_at_i = i * self.hypergeometric_distribution(self.n_max, k, i, j) * self.get_error_prob(
j, self.n_max, self.prrtChannelParameters.loss_rate_fwd)
total_packet_erasure += packet_erasure_at_i
return (1 / k) * total_packet_erasure
def estimate_n_for_k(self, k):
n = k + 1
while self.residual_packet_erasure_rate(k, n, self.prrtApplicationParameters, self.prrtChannelParameters) > self.prrtApplicationParameters.max_residual_loss_rate\
and n <= self.n_max - self.step_size:
n = k + self.step_size
return n
return 0
def residual_packet_erasure_rate(self, k, n, prrtApplicationParameters, prrtChannelParameters):
total_packet_erasure = 0
for i in range(1, k):
for j in range(max(n - k + 1, i), n - k + i):
packet_erasure_at_i = i * self.hypergeometric_distribution(n, k, i, j) * self.get_error_prob(j, n, prrtChannelParameters.loss_rate_fwd)
total_packet_erasure += packet_erasure_at_i
residual_packet_erasure_rate = (1 / k) * total_packet_erasure # Pr(k, n)
return residual_packet_erasure_rate
class HECGreadySearch(AbstractSearch):
def __init__(self, n_p_min, n_p_max, prrtApplicationParameters, prrtChannelParameters, prrtSystemParameters):
self.n_max = 255
self.step_size = 2 # Step size in the estimation of the optimum code word length
self.n_p_min = n_p_min
self.n_p_max = n_p_max
self.prrtApplicationParameters = prrtApplicationParameters
self.prrtChannelParameters = prrtChannelParameters
self.prrtSystemParameters = prrtSystemParameters
self.p_t = self.prrtApplicationParameters.max_residual_loss_rate
self.is_order_ascending = True
pass
def search(self):
ri_opt = math.inf
k_opt = 0
n_opt = 0
n_p_opt = []
ric = prrt.RestrictedIntegerComposition
# Eq.5.9, page 125
fec_delay_min = self.prrtSystemParameters.source_packet_interval + \
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay + \
(self.prrtChannelParameters.rtt_prop_fwd + self.prrtSystemParameters.processing_delay) / 2 + \
self.prrtSystemParameters.packet_loss_detection_delay
# Eq.5.9 page 125 assumung D_sup = 0
req_delay = self.prrtChannelParameters.rtt_prop_fwd + \
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay + \
self.prrtSystemParameters.processing_delay
# Eq.5.10, page 125
n_c_max = math.ceil((self.prrtApplicationParameters.max_latency - fec_delay_min) / req_delay)
# self.k_lim = somewhat(prrtApplicationParameters.loss_tolerance, self.n_max)
# n_c = 0 is the proactive packet
for n_c in range(n_c_max):
# Eq.5.11, k(Nc, D_T), page 125
k_max = min(self.get_k(n_c, req_delay), self.get_k_lim(1, self.n_max))
for k in [1, k_max]:
n = self.estimate_n_for_k(k)
repair_schedule = ric.gen_repair_schedule(n - k, n_c, self.n_p_min, self.n_p_max)
coding_conf = prrt.PrrtCodingConfiguration(n, k, repair_schedule)
ri = coding_conf.get_redundant_information(self.prrtChannelParameters)
if ri < ri_opt:
k_opt = k
n_opt = n
n_p_opt = repair_schedule
return prrt.PrrtCodingConfiguration(n_opt, k_opt, n_p_opt)
def get_k(self, n_c, req_delay):
return math.ceil((self.prrtApplicationParameters.max_latency -
self.n_p_max * self.prrtSystemParameters.redundancy_packet_transmission_delay -
(self.prrtChannelParameters.rtt_prop_fwd + self.prrtSystemParameters.processing_delay) / 2 -
self.prrtSystemParameters.packet_loss_detection_delay -
n_c * req_delay) / self.prrtSystemParameters.source_packet_interval)
def get_k_lim(self, start, end):
mid_point = math.ceil((end - start) / 2)
p_r = self.get_max_coding_block_length(mid_point)
if p_r == self.p_t:
return mid_point
elif p_r > self.p_t:
self.get_k_lim(start, mid_point - 1)
else:
self.get_k_lim(mid_point + 1, end)
# Pr(k, n_max)
def get_max_coding_block_length(self, k):
total_packet_erasure = 0
for i in range(1, k):
for j in range(max(self.n_max - k + 1, i), self.n_max - k + i):
packet_erasure_at_i = i * self.hypergeometric_distribution(self.n_max, k, i, j) * self.get_error_prob(
j, self.n_max, self.prrtChannelParameters.loss_rate_fwd)
total_packet_erasure += packet_erasure_at_i
return (1 / k) * total_packet_erasure
def estimate_n_for_k(self, k):
n = k + 1
while self.residual_packet_erasure_rate(k, n, self.prrtApplicationParameters, self.prrtChannelParameters) > self.prrtApplicationParameters.max_residual_loss_rate\
and n <= self.n_max - self.step_size:
print("")
return 0
def residual_packet_erasure_rate(self, k, n, prrtApplicationParameters, prrtChannelParameters):
total_packet_erasure = 0
for i in range(1, k):
for j in range(max(n - k + 1, i), n - k + i):
packet_erasure_at_i = i * self.hypergeometric_distribution(n, k, i, j) * self.get_error_prob(j, n, prrtChannelParameters.loss_rate_fwd)
total_packet_erasure += packet_erasure_at_i
residual_packet_erasure_rate = (1 / k) * total_packet_erasure # Pr(k, n)
return residual_packet_erasure_rate
\ No newline at end of file
import numpy as np
import pandas as pd
import prrt
from prrt.HecSearch import HECFullSearch
from prrt.HecSearch import HECGreadySearch
import time
def evaluate(searchAlgorithm, appParams, channelParams, systemParams):
n_p_min = 1
n_p_max = get_n_p_max(channelParams.rtt_prop_fwd, channelParams.pkt_length, channelParams.data_rate_btl_fwd)
if searchAlgorithm == "FullSearch":
full_search = HECFullSearch(n_p_min, n_p_max, appParams, channelParams, systemParams)
start = time.now()
full_search_result = full_search.search()
duration = time.now() - start
return [full_search_result, duration]
if searchAlgorithm == "GreedySearch":
greedy_search = HECGreadySearch(n_p_min, n_p_max, appParams, channelParams, systemParams)
start = time.now()
greedy_search_result = greedy_search.search()
duration = time.now() - start
return [greedy_search_result, duration]
def test_case(csv_file_path):
appParams = prrt.PrrtApplicationParameters(max_latency, max_residual_loss_rate, data_rate)
chnlParams = prrt.PrrtChannelParameters(...)
sysParams = prrt.PrrtSystemParameters(...)
for searchAlgorithm in ["FullSearch", "GreedySearch"]:
config, duration = evaluate(searchAlgorithm, appParams, chnlParams, sysParams)
rows.append(..., search, config, duration)
pd.DataFrame(rows).to_csv()
def get_n_p_max(rtt_prop_fwd, pkt_length, data_rate_btl_fwd):
return rtt_prop_fwd * data_rate_btl_fwd / pkt_length
\ No newline at end of file
import pyximport; pyximport.install()
import itertools
def generate_restricted_integer_compositions(redundancy, positions, min, max):
cdef c_redundancy = redundancy
cdef c_positions = positions
cdef c_min = min
cdef c_max = max
if c_positions < 1:
raise StopIteration
if c_positions == 1:
if c_redundancy >= c_min and c_redundancy <= c_max:
yield (c_redundancy,)
raise StopIteration
for i in range(c_min, c_redundancy + 1):
for result in generate_restricted_integer_compositions(c_redundancy - i, c_positions - 1, i, c_max):
if (i <= c_max):
yield (i,) + result
# is_order_ascending = False for full search and True for greedy search
def gen_repair_schedules(redundancy, positions, min, max, is_order_ascending):
arbitrary_schedules = []
ordered_schedules = set()
gen_rics = generate_restricted_integer_compositions(redundancy, positions, min, max)
for ric in gen_rics:
arbitrary_schedules.append(ric)
if not is_order_ascending:
for ric in arbitrary_schedules:
ordered_schedules.add(itertools.permutations(ric))
return list(ordered_schedules)
else:
return arbitrary_schedules
def gen_repair_schedule(redundancy, positions, min, max):
if(redundancy < positions * min or min > max):
raise Exception("Illegal input combinations. Make sure the min > max. And, number of total redundancy is greater that positions*min.")
opt_schedule = [min for p in range(positions)]
redundancy_left_over = redundancy - min * positions
last_index = positions - 1
while(redundancy_left_over > 0):
if(opt_schedule[last_index] < max):
opt_schedule[last_index] += 1
redundancy_left_over -= 1
else:
last_index -= 1
return opt_schedule
\ No newline at end of file
......@@ -4,7 +4,7 @@ import prrt
import time
import numpy as np
dataset_file_path = 'in_12_param_4_sz_mini_1000.csv'
dataset_file_path = 'documents/in_12_param_4_sz_mini_1000.csv'
def get_n_p_max(rtt_prop_fwd, pkt_length, data_rate_btl_fwd):
......
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