def get_latency_stats_tuples(latencies, read_latencies, write_latencies, ping_latencies):
"""returns key stats (mean, median etc) for read, write and (read + write)."""
kv_tuples = []
kv_tuples.extend(get_stat_in_tuples(get_all_values(latencies), 'all'))
kv_tuples.extend(get_stat_in_tuples(get_all_values(read_latencies), 'read'))
kv_tuples.extend(get_stat_in_tuples(get_all_values(write_latencies), 'write'))
kv_tuples.extend(get_stat_in_tuples(ping_latencies, 'ping'))
kv_tuples.extend(get_stat_in_tuples(closest_ns_latencies, 'closest_ns'))
avg_processing_delay = 0
if len(all_tuples) > 0:
avg_processing_delay = total_processing_delay/len(all_tuples)
kv_tuples.append(['Avg-processing-delay', avg_processing_delay])
mean_read_by_ping = 0
readmean = get_value(kv_tuples, 'readmean')
pingmean = get_value(kv_tuples, 'pingmean')
if readmean is not None and pingmean is not None and pingmean != 0:
mean_read_by_ping = readmean / pingmean
median_read_by_ping = 0
readmedian = get_value(kv_tuples, 'readmedian')
pingmedian = get_value(kv_tuples, 'pingmedian')
if readmedian is not None and pingmedian is not None and pingmedian != 0:
median_read_by_ping = readmedian / pingmedian
kv_tuples.append(['mean_read_by_ping', mean_read_by_ping])
kv_tuples.append(['median_read_by_ping', median_read_by_ping])
return kv_tuples
def get_latency_stats_tuples(latencies, read_latencies, write_latencies, ping_latencies):
"""returns key stats (mean, median etc) for read, write and (read + write)."""
kv_tuples = []
kv_tuples.extend(get_stat_in_tuples(get_all_values(latencies), 'all'))
kv_tuples.extend(get_stat_in_tuples(get_all_values(read_latencies), 'read'))
kv_tuples.extend(get_stat_in_tuples(get_all_values(write_latencies), 'write'))
kv_tuples.extend(get_stat_in_tuples(ping_latencies, 'ping'))
kv_tuples.extend(get_stat_in_tuples(closest_ns_latencies, 'closest_ns'))
avg_processing_delay = 0
if len(all_tuples) > 0:
avg_processing_delay = total_processing_delay/len(all_tuples)
kv_tuples.append(['Avg-processing-delay', avg_processing_delay])
mean_read_by_ping = 0
readmean = get_value(kv_tuples, 'readmean')
pingmean = get_value(kv_tuples, 'pingmean')
if readmean is not None and pingmean is not None and pingmean != 0:
mean_read_by_ping = readmean / pingmean
median_read_by_ping = 0
readmedian = get_value(kv_tuples, 'readmedian')
pingmedian = get_value(kv_tuples, 'pingmedian')
if readmedian is not None and pingmedian is not None and pingmedian != 0:
median_read_by_ping = readmedian / pingmedian
kv_tuples.append(['mean_read_by_ping', mean_read_by_ping])
kv_tuples.append(['median_read_by_ping', median_read_by_ping])
return kv_tuples
def output_stats_by_name(all_tuples_filename):
value_index = 4
name_index = 0 # 0 = name, 1 = lns, 2 = ns
# this option removes names for which there is a failed read request
folder = dirname(all_tuples_filename)
exclude_failed_reads = True
if exclude_failed_reads:
failed_reads_names = select_failed_reads_names(all_tuples_filename)
write_array(failed_reads_names.keys(), os.path.join(folder, 'failed_reads_names.txt'))
all_tuples_filename = write_all_tuples_excluding_failed(all_tuples_filename, failed_reads_names)
outfile1 = os.path.join(folder, 'all_by_name.txt')
output_tuples1 = group_by(all_tuples_filename, name_index, value_index)
write_tuple_array(output_tuples1, outfile1, p = True)
outfile2 =os.path.join(folder, 'writes_by_name.txt')
output_tuples2 = group_by(all_tuples_filename, name_index, value_index, filter = write_filter)
write_tuple_array(output_tuples2, outfile2, p = True)
outfile3 = os.path.join(folder, 'reads_by_name.txt')
output_tuples3 = group_by(all_tuples_filename, name_index, value_index, filter = read_filter)
write_tuple_array(output_tuples3, outfile3, p = True)
filenames = [outfile1, outfile2, outfile3]
schemes = ['ALL', 'WRITES', 'READS']
template_file = os.path.join(script_folder, 'template1.gpt')
col_no = 4
pdf_filename = os.path.join(folder, 'median_by_name.pdf')
get_cdf_and_plot(filenames, schemes, [col_no]*len(schemes), pdf_filename, folder, template_file)
col_no = 5
pdf_filename = os.path.join(folder, 'mean_by_name.pdf')
get_cdf_and_plot(filenames, schemes, [col_no]*len(schemes), pdf_filename, folder, template_file)
# output key stats
read_median_list = [t[4] for t in output_tuples3]
read_mean_list = [t[5] for t in output_tuples3]
write_median_list = [t[4] for t in output_tuples2]
write_mean_list = [t[5] for t in output_tuples2]
# delete this.
#read_median_list2 = []
#for v in read_median_list:
# if v <5000:
# read_median_list2.append(v)
kv_tuples = []
kv_tuples.extend(get_stat_in_tuples(read_median_list, 'read_median_names'))
kv_tuples.extend(get_stat_in_tuples(read_mean_list, 'read_mean_names'))
kv_tuples.extend(get_stat_in_tuples(write_median_list, 'write_median_names'))
kv_tuples.extend(get_stat_in_tuples(write_mean_list, 'write_mean_names'))
outputfile = os.path.join(folder, 'latency_stats_names.txt')
write_tuple_array(kv_tuples, outputfile, p = True)
os.system('cat ' + outputfile)
def output_file_stats(output_filename, input_filename, col, prefix):
from select_columns import extract_column_from_file
values = extract_column_from_file(input_filename, col)
from stats import get_stat_in_tuples
output_tuples = get_stat_in_tuples(values, prefix)
from write_array_to_file import write_tuple_array
write_tuple_array(output_tuples, output_filename, p = True)
def output_file_stats(output_filename, input_filename, col, prefix):
from select_columns import extract_column_from_file
values = extract_column_from_file(input_filename, col)
from stats import get_stat_in_tuples
output_tuples = get_stat_in_tuples(values, prefix)
from write_array_to_file import write_tuple_array
write_tuple_array(output_tuples, output_filename, p=True)
def output_latency_stats(output_dir):
"""Output all statistics"""
# older code: output read, write, overall stats
output_stats(latencies, os.path.join(output_dir, 'all'))
output_stats(read_latencies, os.path.join(output_dir, 'read'))
output_stats(write_latencies, os.path.join(output_dir, 'write'))
output_stats(add_latencies, os.path.join(output_dir, 'add'))
output_stats(remove_latencies, os.path.join(output_dir, 'remove'))
output_stats(group_change_latencies, os.path.join(output_dir, 'group_change'))
# output all tuples in a file, used in group-by-name, group-by-time script.
write_tuple_array(all_tuples, os.path.join(output_dir, 'all_tuples.txt'), p=True)
# output ping latencies
write_tuple_array(get_cdf(ping_latencies), os.path.join(output_dir, 'ping_latency.txt'), p=True)
# output closest NS latencies
write_tuple_array(get_cdf(closest_ns_latencies), os.path.join(output_dir, 'closest_ns_latency.txt'), p=True)
# output mean and median query latencies ove time during the experiment.
from output_by_time import output_by_time
output_by_time(output_dir, 'latency_by_time.txt')
# output start and end times
#get_start_end_times(all_tuples, os.path.join(output_dir,'start_end_times.txt'))
# output key stats : mean-latency, median-write-latency-etc.
latency_tuples = get_latency_stats_tuples()
# latencies, read_latencies, write_latencies, add_latencies,
# remove_latencies, ping_latencies
write_tuple_array(latency_tuples, os.path.join(output_dir, 'latency_stats.txt'), p=True)
os.system('cat ' + os.path.join(output_dir, 'latency_stats.txt'))
# experiment summary stats:
write_tuple_array(get_summary_stats(), output_dir + '/summary.txt', p=True)
os.system('cat ' + output_dir + '/summary.txt')
# plot results for this experiment.
plot(output_dir)
if len(time_to_connect_values) > 0:
time_to_connect_stats = get_stat_in_tuples(time_to_connect_values, 'time_to_connect')
timeout_value = 5000
timeout_count = 0
for t in time_to_connect_values:
if t > timeout_value:
timeout_count += 1
fraction_timeout = timeout_count*1.0/len(time_to_connect_values)
time_to_connect_stats.append(['fraction-timeouts', fraction_timeout])
write_tuple_array(time_to_connect_stats, os.path.join(output_dir, 'time_to_connect.txt'), p=True)
os.system('cat ' + os.path.join(output_dir, 'time_to_connect.txt'))
def output_stats_by_name(all_tuples_filename):
value_index = 4
name_index = 0 # 0 = name, 1 = lns, 2 = ns
# this option removes names for which there is a failed read request
folder = dirname(all_tuples_filename)
exclude_failed_reads = True
if exclude_failed_reads:
failed_reads_names = select_failed_reads_names(all_tuples_filename)
write_array(failed_reads_names.keys(),
os.path.join(folder, 'failed_reads_names.txt'))
all_tuples_filename = write_all_tuples_excluding_failed(
all_tuples_filename, failed_reads_names)
outfile1 = os.path.join(folder, 'all_by_name.txt')
output_tuples1 = group_by(all_tuples_filename, name_index, value_index)
write_tuple_array(output_tuples1, outfile1, p=True)
outfile2 = os.path.join(folder, 'writes_by_name.txt')
output_tuples2 = group_by(all_tuples_filename,
name_index,
value_index,
filter=write_filter)
write_tuple_array(output_tuples2, outfile2, p=True)
outfile3 = os.path.join(folder, 'reads_by_name.txt')
output_tuples3 = group_by(all_tuples_filename,
name_index,
value_index,
filter=read_filter)
write_tuple_array(output_tuples3, outfile3, p=True)
filenames = [outfile1, outfile2, outfile3]
schemes = ['ALL', 'WRITES', 'READS']
template_file = os.path.join(script_folder, 'template1.gpt')
col_no = 4
pdf_filename = os.path.join(folder, 'median_by_name.pdf')
get_cdf_and_plot(filenames, schemes, [col_no] * len(schemes), pdf_filename,
folder, template_file)
col_no = 5
pdf_filename = os.path.join(folder, 'mean_by_name.pdf')
get_cdf_and_plot(filenames, schemes, [col_no] * len(schemes), pdf_filename,
folder, template_file)
# output key stats
read_median_list = [t[4] for t in output_tuples3]
read_mean_list = [t[5] for t in output_tuples3]
write_median_list = [t[4] for t in output_tuples2]
write_mean_list = [t[5] for t in output_tuples2]
# delete this.
#read_median_list2 = []
#for v in read_median_list:
# if v <5000:
# read_median_list2.append(v)
kv_tuples = []
kv_tuples.extend(get_stat_in_tuples(read_median_list, 'read_median_names'))
kv_tuples.extend(get_stat_in_tuples(read_mean_list, 'read_mean_names'))
kv_tuples.extend(
get_stat_in_tuples(write_median_list, 'write_median_names'))
kv_tuples.extend(get_stat_in_tuples(write_mean_list, 'write_mean_names'))
outputfile = os.path.join(folder, 'latency_stats_names.txt')
write_tuple_array(kv_tuples, outputfile, p=True)
os.system('cat ' + outputfile)
def parse_dns_output(log_files_dir, output_dir, filter=None):
output_extended_tuple_file(log_files_dir, output_dir)
# plot cdf across requests
tuples_file = os.path.join(output_dir, 'all_tuples.txt')
filenames = [tuples_file]*2
schemes = ['Ultra-DNS', 'LNS-RTT']
#latency_index = 5
#ping_latency_index = 6
# latency index = 4, ping to lns = 5 for this experiment.
col_nos = [6, 7]
pdf_file = os.path.join(output_dir, 'cdf_latency.pdf')
template_file = '/home/abhigyan/gnrs/gpt_files/template1.gpt'
get_cdf_and_plot(filenames, schemes, col_nos, pdf_file, output_dir, template_file)
# plot cdf across names
value_index = 6
name_index = 1 # 0 = lns-query-id, 1 = name-id, 2 = name, 3 = ultra-dns-latency,
outfile1 = os.path.join(output_dir, 'reads_by_name.txt')
output_tuples1 = group_by(tuples_file, name_index, value_index, filter = None)
write_tuple_array(output_tuples1, outfile1, p = True)
value_index = 7
name_index = 1 # 1 = name,
outfile2 = os.path.join(output_dir, 'pings_by_name.txt')
output_tuples2 = group_by(tuples_file, name_index, value_index, filter = None)
write_tuple_array(output_tuples2, outfile2, p = True)
filenames = [outfile1,outfile2]
schemes = ['Ultra-DNS', 'LNS-RTT']
col_nos = [5, 5] # Mean value index = 5
pdf_file = os.path.join(output_dir, 'read_mean_by_name.pdf')
template_file = '/home/abhigyan/gnrs/gpt_files/template1.gpt'
get_cdf_and_plot(filenames, schemes, col_nos, pdf_file, output_dir, template_file)
filenames = [outfile1,outfile2]
schemes = ['Ultra-DNS', 'LNS-RTT']
col_nos = [4, 4] # Median value index = 4
pdf_file = os.path.join(output_dir, 'read_median_by_name.pdf')
template_file = '/home/abhigyan/gnrs/gpt_files/template1.gpt'
get_cdf_and_plot(filenames, schemes, col_nos, pdf_file, output_dir, template_file)
latency_stats = []
from stats import get_stat_in_tuples
latency_stats.extend(get_stat_in_tuples(all_latencies, 'read'))
latency_stats.extend(get_stat_in_tuples(all_latencies, 'read'))
read_median_list = [ t[4] for t in output_tuples1]
read_mean_list = [ t[5] for t in output_tuples1]
latency_stats.extend(get_stat_in_tuples(read_median_list, 'read_median_names'))
latency_stats.extend(get_stat_in_tuples(read_mean_list, 'read_mean_names'))
outputfile = os.path.join(output_dir, 'latency_stats.txt')
write_tuple_array(latency_stats, outputfile, p = True)
os.system('cat ' + outputfile)
## output them hostwise
value_index = 6
name_index = 5 # 0 = lns-query-id, 1 = name-id, 2 = name, 3 = ultra-dns-latency, 4 = hostname
outfile1 = os.path.join(output_dir, 'reads_by_host.txt')
output_tuples1 = group_by(tuples_file, name_index, value_index, filter = None, numeric = False)
write_tuple_array(output_tuples1, outfile1, p = True)
def count_ns_msgs(folder, output_folder):
if not os.path.exists(folder):
print 'ERROR: Folder does not exist'
sys.exit(2)
os.system('gzip -d ' + folder + '/log_ns_*/*gz 2>/dev/null')
host_log_folders = os.listdir(folder)
msg_tuples = []
for host_log in host_log_folders:
if not host_log.startswith("log_ns_"):
continue
# log_ns_2 folder
if os.path.isdir(folder + '/' + host_log + '/log/'):
log_path = folder + '/' + host_log + '/log/'
else:
log_path = folder + '/' + host_log + '/'
# print log_path
node_id = -1
query = -1
update_sent = -1
update_recvd = -1
for i in range(50): # 10 max number of gnrs_stat files
filename = log_path + '/gnrs_stat.xml.' + str(i)
if not os.path.exists(filename):
break
node_id, query, update_sent, update_recvd = update_mgs_stats(filename)
msg_tuples.append([node_id, query, update_sent, update_recvd])
query_tuples = [t[1] for t in msg_tuples]
update_sent_tuples = [t[2] for t in msg_tuples]
update_recvd_tuples = [t[3] for t in msg_tuples]
overall_tuples = [(t[1] + t[2] + t[3]) for t in msg_tuples]
stat_tuples = []
#print 'QUERY', sorted(query_tuples)
#print 'Update Sent', sorted(update_sent_tuples)
#print 'Update Recvd', sorted(update_recvd_tuples)
#print 'Total queries', sum(query_tuples)
#print 'Total updates sent', sum(update_sent_tuples)
#print 'Total updates recvd', sum(update_recvd_tuples)
stat_tuples.append(['Total-queries', sum(query_tuples)])
stat_tuples.append(['Total-updates-recvd', sum(update_recvd_tuples)])
stat_tuples.extend(get_stat_in_tuples(update_recvd_tuples, 'update-recvd'))
query_fairness = get_fairness(query_tuples)
update_sent_fairness = get_fairness(update_sent_tuples)
update_recvd_fairness = get_fairness(update_recvd_tuples)
overall_fairness = get_fairness(overall_tuples)
#print 'Query-Fairness', query_fairness
#print 'Update-Send-Fairness', update_sent_fairness
#print 'Update-Recvd-Fairness', update_recvd_fairness
#print 'Overall-Fairness', overall_fairness
stat_tuples.append(['Query-Fairness', query_fairness])
stat_tuples.append(['Update-Recvd-Fairness', update_recvd_fairness])
stat_tuples.append(['Overall-Fairness', overall_fairness])
output_file = os.path.join(output_folder, 'ns-fairness.txt')
from write_array_to_file import write_tuple_array
write_tuple_array(stat_tuples, output_file, p = True)
os.system('cat ' + output_file)
def count_ns_msgs(folder, output_folder):
if not os.path.exists(folder):
print 'ERROR: Folder does not exist'
sys.exit(2)
os.system('gzip -d ' + folder + '/log_ns_*/*gz 2>/dev/null')
host_log_folders = os.listdir(folder)
msg_tuples = []
for host_log in host_log_folders:
if not host_log.startswith("log_ns_"):
continue
# log_ns_2 folder
if os.path.isdir(folder + '/' + host_log + '/log/'):
log_path = folder + '/' + host_log + '/log/'
else:
log_path = folder + '/' + host_log + '/'
# print log_path
node_id = -1
query = -1
update_sent = -1
update_recvd = -1
for i in range(50): # 10 max number of gnrs_stat files
filename = log_path + '/gnrs_stat.xml.' + str(i)
if not os.path.exists(filename):
break
node_id, query, update_sent, update_recvd = update_mgs_stats(
filename)
msg_tuples.append([node_id, query, update_sent, update_recvd])
query_tuples = [t[1] for t in msg_tuples]
update_sent_tuples = [t[2] for t in msg_tuples]
update_recvd_tuples = [t[3] for t in msg_tuples]
overall_tuples = [(t[1] + t[2] + t[3]) for t in msg_tuples]
stat_tuples = []
#print 'QUERY', sorted(query_tuples)
#print 'Update Sent', sorted(update_sent_tuples)
#print 'Update Recvd', sorted(update_recvd_tuples)
#print 'Total queries', sum(query_tuples)
#print 'Total updates sent', sum(update_sent_tuples)
#print 'Total updates recvd', sum(update_recvd_tuples)
stat_tuples.append(['Total-queries', sum(query_tuples)])
stat_tuples.append(['Total-updates-recvd', sum(update_recvd_tuples)])
stat_tuples.extend(get_stat_in_tuples(update_recvd_tuples, 'update-recvd'))
query_fairness = get_fairness(query_tuples)
update_sent_fairness = get_fairness(update_sent_tuples)
update_recvd_fairness = get_fairness(update_recvd_tuples)
overall_fairness = get_fairness(overall_tuples)
#print 'Query-Fairness', query_fairness
#print 'Update-Send-Fairness', update_sent_fairness
#print 'Update-Recvd-Fairness', update_recvd_fairness
#print 'Overall-Fairness', overall_fairness
stat_tuples.append(['Query-Fairness', query_fairness])
stat_tuples.append(['Update-Recvd-Fairness', update_recvd_fairness])
stat_tuples.append(['Overall-Fairness', overall_fairness])
output_file = os.path.join(output_folder, 'ns-fairness.txt')
from write_array_to_file import write_tuple_array
write_tuple_array(stat_tuples, output_file, p=True)
os.system('cat ' + output_file)
