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,
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.plot", rampup_value,
mean_group)
utils.write_gnuplot_file(current_dir + "/sum.plot", 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, 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.plot", rampup_value, mean_group)
utils.write_gnuplot_file(current_dir+"/sum.plot", 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)