def test_init_state(self):
state = c.init_state(100, [20, 40], 2)
self.assertEquals(state['previous_state'], 0)
self.assertEquals(state['previous_utilization'], [])
self.assertEquals(state['time_in_states'], 0)
self.assertEquals(state['time_in_state_n'], 0)
self.assertTrue('request_windows' in state)
self.assertTrue('estimate_windows' in state)
self.assertTrue('variances' in state)
self.assertTrue('acceptable_variances' in state)
def test_init_state(self):
state = c.init_state(100, [20, 40], 2)
self.assertEquals(state['previous_state'], 0)
self.assertEquals(state['previous_utilization'], [])
self.assertEquals(state['time_in_states'], 0)
self.assertEquals(state['time_in_state_n'], 0)
self.assertTrue('request_windows' in state)
self.assertTrue('estimate_windows' in state)
self.assertTrue('variances' in state)
self.assertTrue('acceptable_variances' in state)
def test_mhod(self):
state_config = [1.0]
otf = 0.1
window_sizes = [30, 40]
bruteforce_step = 0.5
learning_steps = 0
time_step = 300
migration_time = 20.
utilization = [1.0]
state = c.init_state(10, window_sizes, 2)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 0.],
[0., 0.]])
expect(c).get_current_state.and_return(1).once()
decision, _ = c.mhod(state_config, otf, window_sizes,
bruteforce_step, learning_steps, time_step,
migration_time, utilization, state)
self.assertFalse(decision)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 0.],
[0., 0.]])
expect(c).get_current_state.and_return(0).once()
decision, _ = c.mhod(state_config, otf, window_sizes,
bruteforce_step, learning_steps, time_step,
migration_time, utilization, state)
self.assertFalse(decision)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 1.],
[0., 1.]])
expect(c).get_current_state.and_return(0).once()
decision, _ = c.mhod(state_config, otf, window_sizes,
bruteforce_step, learning_steps, time_step,
migration_time, utilization, state)
self.assertFalse(decision)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 1.],
[0., 1.]])
expect(c).get_current_state.and_return(1).once()
decision, _ = c.mhod(state_config, otf, window_sizes,
bruteforce_step, learning_steps, time_step,
migration_time, utilization, state)
self.assertTrue(decision)
def test_mhod(self):
state_config = [1.0]
otf = 0.1
window_sizes = [30, 40]
bruteforce_step = 0.5
learning_steps = 0
time_step = 300
migration_time = 20.
utilization = [1.0]
state = c.init_state(10, window_sizes, 2)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 0.], [0., 0.]])
expect(c).get_current_state.and_return(1).once()
decision, _ = c.mhod(state_config, otf, window_sizes, bruteforce_step,
learning_steps, time_step, migration_time, utilization, state)
self.assertFalse(decision)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 0.], [0., 0.]])
expect(c).get_current_state.and_return(0).once()
decision, _ = c.mhod(state_config, otf, window_sizes, bruteforce_step,
learning_steps, time_step, migration_time, utilization, state)
self.assertFalse(decision)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 1.], [0., 1.]])
expect(c).get_current_state.and_return(0).once()
decision, _ = c.mhod(state_config, otf, window_sizes, bruteforce_step,
learning_steps, time_step, migration_time, utilization, state)
self.assertFalse(decision)
with MockTransaction:
state['previous_utilization'] = []
expect(estimation).select_best_estimates.and_return([[0., 1.], [0., 1.]])
expect(c).get_current_state.and_return(1).once()
decision, _ = c.mhod(state_config, otf, window_sizes, bruteforce_step,
learning_steps, time_step, migration_time, utilization, state)
self.assertTrue(decision)