x/terracotta
Code Issues Proposed changes

Added a threshold parameter to the loess overload detection algorithms

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This commit is contained in:
Anton Beloglazov 2012-11-26 15:18:23 +11:00
parent 265c6420ce
commit b6f54366fe
3 changed files with 29 additions and 14 deletions
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2
neat.conf
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@ -117,12 +117,14 @@ algorithm_underload_detection_parameters = {"threshold": 0.5, "n": 2}
#algorithm_overload_detection_factory = neat.locals.overload.trivial.threshold_factory
#algorithm_overload_detection_factory = neat.locals.overload.mhod.core.mhod_factory
algorithm_overload_detection_factory = neat.locals.overload.trivial.last_n_average_threshold_factory
#algorithm_overload_detection_factory = neat.locals.overload.statistics.loess_factory
# A JSON encoded parameters, which will be parsed and passed to the
# specified overload detection algorithm factory
#algorithm_overload_detection_parameters = {"threshold": 0.9}
#algorithm_overload_detection_parameters = {"state_config": [0.95], "otf": 0.1, "window_sizes": [30, 40, 50, 60, 70, 80, 90, 100], "bruteforce_step": 0.2, "learning_steps": 30}
algorithm_overload_detection_parameters = {"threshold": 0.95, "n": 2}
#algorithm_overload_detection_parameters = {"threshold": 0.8, "param": 1.0, "length": 30}
# The fully qualified name of a Python factory function that returns a
# function implementing a VM selection algorithm

25
neat/locals/overload/statistics.py
View File

@ -44,7 +44,8 @@ def loess_factory(time_step, migration_time, params):
"""
migration_time_normalized = float(migration_time) / time_step
return lambda utilization, state=None: \
(loess(params['param'],
(loess(params['threshold'],
params['param'],
params['length'],
migration_time_normalized,
utilization),
@ -69,7 +70,8 @@ def loess_robust_factory(time_step, migration_time, params):
"""
migration_time_normalized = float(migration_time) / time_step
return lambda utilization, state=None: \
(loess_robust(params['param'],
(loess_robust(params['threshold'],
params['param'],
params['length'],
migration_time_normalized,
utilization),
@ -123,9 +125,12 @@ def iqr_threshold_factory(time_step, migration_time, params):
@contract
def loess(param, length, migration_time, utilization):
def loess(threshold, param, length, migration_time, utilization):
""" The Loess based overload detection algorithm.
:param threshold: The CPU utilization threshold.
:type threshold: float
:param param: The safety parameter.
:type param: float
@ -142,6 +147,7 @@ def loess(param, length, migration_time, utilization):
:rtype: bool
"""
return loess_abstract(loess_parameter_estimates,
threshold,
param,
length,
migration_time,
@ -149,9 +155,12 @@ def loess(param, length, migration_time, utilization):
@contract
def loess_robust(param, length, migration_time, utilization):
def loess_robust(threshold, param, length, migration_time, utilization):
""" The robust Loess based overload detection algorithm.
:param threshold: The CPU utilization threshold.
:type threshold: float
:param param: The safety parameter.
:type param: float
@ -168,6 +177,7 @@ def loess_robust(param, length, migration_time, utilization):
:rtype: bool
"""
return loess_abstract(loess_robust_parameter_estimates,
threshold,
param,
length,
migration_time,
@ -175,12 +185,15 @@ def loess_robust(param, length, migration_time, utilization):
@contract
def loess_abstract(estimator, param, length, migration_time, utilization):
def loess_abstract(estimator, threshold, param, length, migration_time, utilization):
""" The abstract Loess algorithm.
:param estimator: A parameter estimation function.
:type estimator: function
:param threshold: The CPU utilization threshold.
:type threshold: float
:param param: The safety parameter.
:type param: float
@ -200,7 +213,7 @@ def loess_abstract(estimator, param, length, migration_time, utilization):
return False
estimates = estimator(utilization[-length:])
prediction = (estimates[0] + estimates[1] * (length + migration_time))
return param * prediction >= 1.
return param * prediction >= threshold
@contract

16
tests/locals/overload/test_statistics.py
View File

@ -25,7 +25,7 @@ class Statistics(TestCase):
def test_loess_factory(self):
alg = stats.loess_factory(
300, 20., {'param': 1.2, 'length': 3})
300, 20., {'threshold': 1.0, 'param': 1.2, 'length': 3})
self.assertEqual(alg([]), (False, {}))
data = [1.05, 1.09, 1.07, 1.12, 1.02, 1.18,
@ -39,7 +39,7 @@ class Statistics(TestCase):
def test_loess_robust_factory(self):
alg = stats.loess_robust_factory(
300, 20., {'param': 1.2, 'length': 3})
300, 20., {'threshold': 1.0, 'param': 1.2, 'length': 3})
self.assertEqual(alg([]), (False, {}))
data = [1.05, 1.09, 1.07, 1.12, 1.02, 1.18,
@ -78,28 +78,28 @@ class Statistics(TestCase):
self.assertEqual(alg([0., 0., 1.0]), (True, {}))
def test_loess(self):
assert not stats.loess(1.2, 3, 0.5, [])
assert not stats.loess(1.0, 1.2, 3, 0.5, [])
data = [1.05, 1.09, 1.07, 1.12, 1.02, 1.18,
1.15, 1.04, 1.10, 1.16, 1.08]
assert stats.loess(1.2, 3, 0.5, data)
assert stats.loess(1.0, 1.2, 3, 0.5, data)
data = [0.55, 0.60, 0.62, 0.59, 0.67, 0.73, 0.85, 0.97, 0.73,
0.68, 0.69, 0.52, 0.51, 0.55, 0.48, 0.46, 0.52, 0.55,
0.58, 0.65, 0.70]
assert not stats.loess(1.2, 3, 0.5, data)
assert not stats.loess(1.0, 1.2, 3, 0.5, data)
def test_loess_robust(self):
assert not stats.loess_robust(1.2, 3, 0.5, [])
assert not stats.loess_robust(1.0, 1.2, 3, 0.5, [])
data = [1.05, 1.09, 1.07, 1.12, 1.02, 1.18,
1.15, 1.04, 1.10, 1.16, 1.08]
assert stats.loess_robust(1.2, 3, 0.5, data)
assert stats.loess_robust(1.0, 1.2, 3, 0.5, data)
data = [0.55, 0.60, 0.62, 0.59, 0.67, 0.73, 0.85, 0.97, 0.73,
0.68, 0.69, 0.52, 0.51, 0.55, 0.48, 0.46, 0.52, 0.55,
0.58, 0.65, 0.70]
assert not stats.loess_robust(1.2, 3, 0.5, data)
assert not stats.loess_robust(1.0, 1.2, 3, 0.5, data)
def test_mad_threshold(self):
with MockTransaction: