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authorTim Rault <tim.rault@cengn.ca>2016-07-15 16:32:51 -0400
committerTim Rault <tim.rault@cengn.ca>2016-07-15 16:41:16 -0400
commitaa20b986cebf031489f4280988b4574a9acbc647 (patch)
tree354c6b521d83735a1be29183b447d3b35bf320ac /storperf
parent2227414bd57f4b7f5f275d915fa8f6a2aa21f8f7 (diff)
Add Steady State Detection module
Added a Steady State Detection module containing a steady_state(data_series) function that is able to return a boolean indicating wether or not steady state is reached with the data_series being passed. This module requires a data_treatment(data_series) and an average(data_series) modules that have been added in this commit as well. The data treatment function aims at formatting the data series that is passed to the high level steady_state function to reach the requirement of each sub-module (slope, average and range). Modified the Slope and Range functions so they return None when passed an empty data series instead of 0 which was wrong. Modified the corresponding test cases. Modified the math_range_test.py file to fix a bug in the 2 last tests. Change-Id: I9c3854cb0a21cc37b0787b8afca0821eefaa203d JIRA: STORPERF-60 JIRA: STORPERF-59 JIRA: STORPERF-61 JIRA: STORPERF-62 Signed-off-by: Tim Rault <tim.rault@cengn.ca>
Diffstat (limited to 'storperf')
-rw-r--r--storperf/utilities/data_treatment.py39
-rw-r--r--storperf/utilities/math.py27
-rw-r--r--storperf/utilities/steady_state.py45
3 files changed, 109 insertions, 2 deletions
diff --git a/storperf/utilities/data_treatment.py b/storperf/utilities/data_treatment.py
new file mode 100644
index 0000000..2368fd9
--- /dev/null
+++ b/storperf/utilities/data_treatment.py
@@ -0,0 +1,39 @@
+##############################################################################
+# Copyright (c) 2016 CENGN and others.
+#
+# All rights reserved. This program and the accompanying materials
+# are made available under the terms of the Apache License, Version 2.0
+# which accompanies this distribution, and is available at
+# http://www.apache.org/licenses/LICENSE-2.0
+##############################################################################
+
+
+def data_treatment(data_series):
+ """
+ This function aims at performing any necessary pre treatment on the
+ data_series passed to the steady_state function before being passed
+ under to the different math utilities (slope, range and average)
+ so the data can match the requirements of each algorithm.
+ The function returns a dictionary composed of three values that can be
+ accessed with the following keys : 'slope_data', 'range_data' and
+ 'average_data'.
+ The data_series is currently assumed to follow the pattern :
+ [[x1,y1], [x2,y2], ..., [xm,ym]]. If this pattern were to change, or
+ the input data pattern of one of the math module, this data_treatment
+ function should be the only part of the Steady State detection module
+ that would need to be modified too.
+ """
+
+ x_values = []
+ y_values = []
+ for l in data_series:
+ x_values.append(l[0])
+ y_values.append(l[1])
+
+ treated_data = {
+ 'slope_data': data_series, # No treatment necessary so far
+ 'range_data': y_values, # The y_values only
+ 'average_data': y_values
+ }
+
+ return treated_data
diff --git a/storperf/utilities/math.py b/storperf/utilities/math.py
index 031fc3e..a11ec19 100644
--- a/storperf/utilities/math.py
+++ b/storperf/utilities/math.py
@@ -25,7 +25,7 @@ def slope(data_series):
# In the particular case of an empty data series
if len(data_series) == 0:
- beta2 = 0
+ beta2 = None
else: # The general case
m = len(data_series)
@@ -78,7 +78,7 @@ def range_value(data_series):
# In the particular case of an empty data series
if len(data_series) == 0:
- range_value = 0
+ range_value = None
else: # The general case
max_value = max(data_series)
@@ -86,3 +86,26 @@ def range_value(data_series):
range_value = max_value - min_value
return range_value
+
+
+def average(data_series):
+ """
+ This function seeks to calculate the average value of the data series
+ given a series following the pattern : [y1, y2, y3, ..., ym].
+ If this data pattern were to change, the data_treatment function
+ should be adjusted to ensure compatibility with the rest of the
+ Steady State Dectection module.
+ The function returns a float number corresponding to the average of the yi.
+ """
+ m = len(data_series)
+
+ if m == 0: # In the particular case of an empty data series
+ average = None
+
+ else:
+ data_sum = 0
+ for value in data_series:
+ data_sum += value
+ average = data_sum / float(m)
+
+ return average
diff --git a/storperf/utilities/steady_state.py b/storperf/utilities/steady_state.py
new file mode 100644
index 0000000..8bfcb93
--- /dev/null
+++ b/storperf/utilities/steady_state.py
@@ -0,0 +1,45 @@
+##############################################################################
+# Copyright (c) 2016 CENGN and others.
+#
+# All rights reserved. This program and the accompanying materials
+# are made available under the terms of the Apache License, Version 2.0
+# which accompanies this distribution, and is available at
+# http://www.apache.org/licenses/LICENSE-2.0
+##############################################################################
+from storperf.utilities import data_treatment as DataTreatment
+from storperf.utilities import math as math
+
+
+def steady_state(data_series):
+ """
+ This function seeks to detect steady state given on a measurement
+ window given the data series of that measurement window following
+ the pattern : [[x1,y1], [x2,y2], ..., [xm,ym]]. m represents the number
+ of points recorded in the measurement window, x which represents the
+ time, and y which represents the Volume performance variable being
+ tested e.g. IOPS, latency...
+ The function returns a boolean describing wether or not steady state
+ has been reached with the data that is passed to it.
+ """
+
+ # Pre conditioning the data to match the algorithms
+ treated_data = DataTreatment.data_treatment(data_series)
+
+ # Calculating useful values invoking dedicated functions
+ slope_value = math.slope(treated_data['slope_data'])
+ range_value = math.range_value(treated_data['range_data'])
+ average_value = math.average(treated_data['average_data'])
+
+ if (slope_value is not None and range_value is not None and
+ average_value is not None):
+ # Verification of the Steady State conditions following the SNIA
+ # definition
+ range_condition = range_value < 0.20 * abs(average_value)
+ slope_condition = slope_value < 0.10 * abs(average_value)
+
+ steady_state = range_condition and slope_condition
+
+ else:
+ steady_state = False
+
+ return steady_state