def test_generator(self):
periods = tg.uniform_int(10, 100)
utils = tg.exponential(0.1, 0.9, 0.3)
g = tg.TaskGenerator(periods, utils)
self.assertEqual(len(list(g.tasks(max_tasks=10))), 10)
self.assertLessEqual(len(list(g.tasks(max_util=10))), 100)
ts1 = g.tasks(max_util=10, squeeze=True, time_conversion=ms2us)
ts2 = g.tasks(max_util=10, squeeze=False, time_conversion=ms2us)
self.assertAlmostEqual(sum([t.utilization() for t in ts1]), 10, places=2)
self.assertNotEqual(sum([t.utilization() for t in ts2]), 10)
def test_task_system_creation(self):
periods = tg.uniform_int(10, 100)
utils = tg.exponential(0.1, 0.9, 0.3)
g = tg.TaskGenerator(periods, utils)
self.assertEqual(len(g.make_task_set(max_tasks=10)), 10)
self.assertLessEqual(len((g.make_task_set(max_util=10))), 100)
ts1 = g.make_task_set(max_util=10, squeeze=True, time_conversion=ms2us)
ts2 = g.make_task_set(max_util=10, squeeze=False, time_conversion=ms2us)
self.assertAlmostEqual(ts1.utilization(), 10, places=2)
# Not strictly impossible, but very unlikely
self.assertNotEqual(ts2.utilization(), 10)
def test_generator(self):
periods = tg.uniform_int(10, 100)
utils = tg.exponential(0.1, 0.9, 0.3)
g = tg.TaskGenerator(periods, utils)
self.assertEqual(len(list(g.tasks(max_tasks=10))), 10)
self.assertLessEqual(len(list(g.tasks(max_util=10))), 100)
ts1 = g.tasks(max_util=10, squeeze=True, time_conversion=ms2us)
ts2 = g.tasks(max_util=10, squeeze=False, time_conversion=ms2us)
self.assertAlmostEqual(sum([t.utilization() for t in ts1]),
10,
places=2)
self.assertNotEqual(sum([t.utilization() for t in ts2]), 10)
def test_drawing_functions(self):
f = tg.uniform_int(10, 100)
self.assertTrue(type(f()) == int)
self.assertTrue(10 <= f() <= 100)
f = tg.uniform(10, 100)
self.assertTrue(type(f()) == float)
self.assertTrue(10 <= f() <= 100)
f = tg.uniform_choice("abcdefg")
self.assertTrue(type(f()) == str)
self.assertTrue("a" <= f() <= "g")
f = tg.exponential(0.1, 0.7, 0.4)
self.assertTrue(type(f()) == float)
self.assertTrue(0.1 <= f() <= 0.7)
def test_drawing_functions(self):
f = tg.uniform_int(10, 100)
self.assertTrue(type(f()) == int)
self.assertTrue(10 <= f() <= 100)
f = tg.uniform(10, 100)
self.assertTrue(type(f()) == float)
self.assertTrue(10 <= f() <= 100)
f = tg.uniform_choice("abcdefg")
self.assertTrue(type(f()) == str)
self.assertTrue('a' <= f() <= 'g')
f = tg.exponential(0.1, 0.7, 0.4)
self.assertTrue(type(f()) == float)
self.assertTrue(0.1 <= f() <= 0.7)
def test_task_system_creation(self):
periods = tg.uniform_int(10, 100)
utils = tg.exponential(0.1, 0.9, 0.3)
g = tg.TaskGenerator(periods, utils)
self.assertEqual(len(g.make_task_set(max_tasks=10)), 10)
self.assertLessEqual(len((g.make_task_set(max_util=10))), 100)
ts1 = g.make_task_set(max_util=10, squeeze=True, time_conversion=ms2us)
ts2 = g.make_task_set(max_util=10,
squeeze=False,
time_conversion=ms2us)
self.assertAlmostEqual(ts1.utilization(), 10, places=2)
# Not strictly impossible, but very unlikely
self.assertNotEqual(ts2.utilization(), 10)
'uni-short' : gen.uniform_int( 3, 33),
'uni-moderate' : gen.uniform_int(10, 100),
'uni-long' : gen.uniform_int(50, 250),
'log-uni-short' : gen.log_uniform_int( 3, 33),
'log-uni-moderate' : gen.log_uniform_int(10, 100),
'log-uni-long' : gen.log_uniform_int(50, 250),
}
NAMED_UTILIZATIONS = {
# Named utilization distributions used in several UNC papers, in milliseconds.
'uni-light' : gen.uniform(0.001, 0.1),
'uni-medium' : gen.uniform(0.1 , 0.4),
'uni-heavy' : gen.uniform(0.5 , 0.9),
'exp-light' : gen.exponential(0, 1, 0.10),
'exp-medium' : gen.exponential(0, 1, 0.25),
'exp-heavy' : gen.exponential(0, 1, 0.50),
'bimo-light' : gen.multimodal([(gen.uniform(0.001, 0.5), 8),
(gen.uniform(0.5 , 0.9), 1)]),
'bimo-medium' : gen.multimodal([(gen.uniform(0.001, 0.5), 6),
(gen.uniform(0.5 , 0.9), 3)]),
'bimo-heavy' : gen.multimodal([(gen.uniform(0.001, 0.5), 4),
(gen.uniform(0.5 , 0.9), 5)]),
}
def uniform_slack(min_slack_ratio, max_slack_ratio):
"""Choose deadlines uniformly such that the slack
is within [cost + min_slack_ratio * (period - cost),
cost + max_slack_ratio * (period - cost)].
# Named period distributions used in several UNC papers, in milliseconds.
'uni-short': gen.uniform_int(3, 33),
'uni-moderate': gen.uniform_int(10, 100),
'uni-long': gen.uniform_int(50, 250),
}
NAMED_UTILIZATIONS = {
# Named utilization distributions used in several UNC papers, in milliseconds.
'uni-light':
gen.uniform(0.001, 0.1),
'uni-medium':
gen.uniform(0.1, 0.4),
'uni-heavy':
gen.uniform(0.5, 0.9),
'exp-light':
gen.exponential(0, 1, 0.10),
'exp-medium':
gen.exponential(0, 1, 0.25),
'exp-heavy':
gen.exponential(0, 1, 0.50),
'bimo-light':
gen.multimodal([(gen.uniform(0.001, 0.5), 8), (gen.uniform(0.5, 0.9), 1)]),
'bimo-medium':
gen.multimodal([(gen.uniform(0.001, 0.5), 6), (gen.uniform(0.5, 0.9), 3)]),
'bimo-heavy':
gen.multimodal([(gen.uniform(0.001, 0.5), 4), (gen.uniform(0.5, 0.9), 5)]),
}
def uniform_slack(min_slack_ratio, max_slack_ratio):
"""Choose deadlines uniformly such that the slack