def compute_metric(current_dir, rampup_value, metric, metric_name):
array = numpy.array(metric)
mean_group = numpy.mean(metric)
deviance_percentage = compute_deviance_percentage(metric)
deviance = numpy.std(array)
utils.write_gnuplot_file(current_dir + "/%s-mean.plot" % metric_name, rampup_value, mean_group)
utils.write_gnuplot_file(
current_dir + "/%s-deviance_percentage.plot" % metric_name, rampup_value, deviance_percentage
)
utils.write_gnuplot_file(current_dir + "/%s-deviance.plot" % metric_name, rampup_value, deviance)
def compute_metric(current_dir, rampup_value, metric, metric_name):
array = numpy.array(metric)
mean_group = numpy.mean(metric)
deviance_percentage = compute_deviance_percentage(metric)
deviance = numpy.std(array)
utils.write_gnuplot_file(current_dir + "/%s-mean.plot" % metric_name,
rampup_value, mean_group)
utils.write_gnuplot_file(
current_dir + "/%s-deviance_percentage.plot" % metric_name,
rampup_value, deviance_percentage)
utils.write_gnuplot_file(current_dir + "/%s-deviance.plot" % metric_name,
rampup_value, deviance)
def print_perf(tolerance_min, tolerance_max, item, df, mode, title,
consistent=None, curious=None, unstable=None,
sub_graph="", rampup_value=0, current_dir=""):
# Tolerance_min represents the min where variance
# shall be considered (in %)
# Tolerance_max represents the maximum that variance
# represent regarding the average (in %)
variance_group = item.std()
mean_group = item.mean()
sum_group = item.sum()
min_group = mean_group - 2 * variance_group
max_group = mean_group + 2 * variance_group
utils.do_print(mode, utils.Levels.INFO,
"%-12s : Group performance : min=%8.2f, mean=%8.2f, "
"max=%8.2f, stddev=%8.2f", title, item.min(),
mean_group, item.max(), variance_group)
variance_tolerance = compute_deviance_percentage(title, df.transpose())
if (rampup_value > 0) and (current_dir):
utils.write_gnuplot_file(current_dir + "/deviance.plot",
rampup_value, variance_group)
utils.write_gnuplot_file(current_dir + "/deviance_percentage.plot",
rampup_value, variance_tolerance)
utils.write_gnuplot_file(current_dir + "/mean%s.plot" % sub_graph,
rampup_value, mean_group)
utils.write_gnuplot_file(current_dir + "/sum%s.plot" % sub_graph,
rampup_value, sum_group)
if variance_tolerance > tolerance_max:
utils.do_print(mode, utils.Levels.ERROR,
"%-12s : Group's variance is too important : %7.2f%% "
"of %7.2f whereas limit is set to %3.2f%%", title,
variance_tolerance, mean_group, tolerance_max)
utils.do_print(mode, utils.Levels.ERROR,
"%-12s : Group performance : UNSTABLE", title)
for host in df.columns:
if host not in curious:
unstable.append(host)
else:
curious_performance = False
for host in df.columns:
if (("loops_per_sec") in mode) or ("bogomips" in mode):
mean_host = df[host][title].mean()
else:
mean_host = df[host].mean()
# If the variance is very low, don't try to find the black sheep
if variance_tolerance > tolerance_min:
if mean_host > max_group:
curious_performance = True
utils.do_print(
mode, utils.Levels.WARNING,
"%-12s : %s : Curious overperformance %7.2f : "
"min_allow_group = %.2f, mean_group = %.2f "
"max_allow_group = %.2f", title, host, mean_host,
min_group, mean_group, max_group)
if host not in curious:
curious.append(host)
if host in consistent:
consistent.remove(host)
elif mean_host < min_group:
curious_performance = True
utils.do_print(
mode, utils.Levels.WARNING,
"%-12s : %s : Curious underperformance %7.2f : "
"min_allow_group = %.2f, mean_group = %.2f "
"max_allow_group = %.2f", title, host, mean_host,
min_group, mean_group, max_group)
if host not in curious:
curious.append(host)
if host in consistent:
consistent.remove(host)
else:
if (host not in consistent) and (host not in curious):
consistent.append(host)
else:
if (host not in consistent) and (host not in curious):
consistent.append(host)
unit = " "
if "Effi." in title:
unit = "%"
if curious_performance is False:
utils.do_print(
mode, utils.Levels.INFO,
"%-12s : Group performance = %7.2f %s : CONSISTENT",
title, mean_group, unit)
else:
utils.do_print(mode, utils.Levels.WARNING,
"%-12s : Group performance = %7.2f %s : SUSPICIOUS",
title, mean_group, unit)
def print_perf(tolerance_min, tolerance_max, item, df, mode, title,
consistent=None, curious=None, unstable=None,
sub_graph="", rampup_value=0, current_dir=""):
# Tolerance_min represents the min where variance
# shall be considered (in %)
# Tolerance_max represents the maximum that variance
# represent regarding the average (in %)
variance_group = item.std()
mean_group = item.mean()
sum_group = item.sum()
min_group = mean_group - 2 * variance_group
max_group = mean_group + 2 * variance_group
utils.do_print(mode, utils.Levels.INFO,
"%-12s : Group performance : min=%8.2f, mean=%8.2f, "
"max=%8.2f, stddev=%8.2f", title, item.min(),
mean_group, item.max(), variance_group)
variance_tolerance = compute_deviance_percentage(title, df.transpose())
if (rampup_value > 0) and (current_dir):
utils.write_gnuplot_file(current_dir + "/deviance.plot",
rampup_value, variance_group)
utils.write_gnuplot_file(current_dir + "/deviance_percentage.plot",
rampup_value, variance_tolerance)
utils.write_gnuplot_file(current_dir + "/mean%s.plot"% sub_graph, rampup_value,
mean_group)
utils.write_gnuplot_file(current_dir + "/sum%s.plot" % sub_graph, rampup_value,
sum_group)
if variance_tolerance > tolerance_max:
utils.do_print(mode, utils.Levels.ERROR,
"%-12s : Group's variance is too important : %7.2f%% "
"of %7.2f whereas limit is set to %3.2f%%", title,
variance_tolerance, mean_group, tolerance_max)
utils.do_print(mode, utils.Levels.ERROR,
"%-12s : Group performance : UNSTABLE", title)
for host in df.columns:
if host not in curious:
unstable.append(host)
else:
curious_performance = False
for host in df.columns:
if (("loops_per_sec") in mode) or ("bogomips" in mode):
mean_host = df[host][title].mean()
else:
mean_host = df[host].mean()
# If the variance is very low, don't try to find the black sheep
if variance_tolerance > tolerance_min:
if mean_host > max_group:
curious_performance = True
utils.do_print(
mode, utils.Levels.WARNING,
"%-12s : %s : Curious overperformance %7.2f : "
"min_allow_group = %.2f, mean_group = %.2f "
"max_allow_group = %.2f", title, host, mean_host,
min_group, mean_group, max_group)
if host not in curious:
curious.append(host)
if host in consistent:
consistent.remove(host)
elif mean_host < min_group:
curious_performance = True
utils.do_print(
mode, utils.Levels.WARNING,
"%-12s : %s : Curious underperformance %7.2f : "
"min_allow_group = %.2f, mean_group = %.2f "
"max_allow_group = %.2f", title, host, mean_host,
min_group, mean_group, max_group)
if host not in curious:
curious.append(host)
if host in consistent:
consistent.remove(host)
else:
if (host not in consistent) and (host not in curious):
consistent.append(host)
else:
if (host not in consistent) and (host not in curious):
consistent.append(host)
unit = " "
if "Effi." in title:
unit = "%"
if curious_performance is False:
utils.do_print(
mode, utils.Levels.INFO,
"%-12s : Group performance = %7.2f %s : CONSISTENT",
title, mean_group, unit)
else:
utils.do_print(mode, utils.Levels.WARNING,
"%-12s : Group performance = %7.2f %s : SUSPICIOUS",
title, mean_group, unit)
