def init_state(history_size, window_sizes, number_of_states):
"""Initialize the state dictionary of the MHOD algorithm.
:param history_size: The number of last system states to store.
:param window_sizes: The required window sizes.
:param number_of_states: The number of states.
:return: The initialization state dictionary.
"""
return {
'previous_state':
0,
'previous_utilization': [],
'time_in_states':
0,
'time_in_state_n':
0,
'request_windows':
estimation.init_request_windows(number_of_states, max(window_sizes)),
'estimate_windows':
estimation.init_deque_structure(window_sizes, number_of_states),
'variances':
estimation.init_variances(window_sizes, number_of_states),
'acceptable_variances':
estimation.init_variances(window_sizes, number_of_states)
}
def init_state(history_size, window_sizes, number_of_states):
"""Initialize the state dictionary of the MHOD algorithm.
:param history_size: The number of last system states to store.
:param window_sizes: The required window sizes.
:param number_of_states: The number of states.
:return: The initialization state dictionary.
"""
return {
'previous_state': 0,
'previous_utilization': [],
'time_in_states': 0,
'time_in_state_n': 0,
'request_windows': estimation.init_request_windows(
number_of_states, max(window_sizes)),
'estimate_windows': estimation.init_deque_structure(
window_sizes, number_of_states),
'variances': estimation.init_variances(
window_sizes, number_of_states),
'acceptable_variances': estimation.init_variances(
window_sizes, number_of_states)}
def mhod(state_config, otf, window_sizes, bruteforce_step, learning_steps,
time_step, migration_time, utilization, state):
"""The MHOD algorithm returning whether the host is overloaded.
:param state_config: The state configuration.
:param otf: The OTF parameter.
:param window_sizes: A list of window sizes.
:param bruteforce_step: The step of the bruteforce algorithm.
:param time_step: The length of the simulation time step in seconds.
:param migration_time: The VM migration time in time seconds.
:param utilization: The history of the host's CPU utilization.
:param state: The state of the algorithm.
:return: The updated state and decision of the algorithm.
"""
utilization_length = len(utilization)
# if utilization_length == state['time_in_states'] and \
# utilization == state['previous_utilization']:
# # No new utilization values
# return False, state
number_of_states = len(state_config) + 1
previous_state = 0
# state['previous_utilization'] = utilization
state['request_windows'] = estimation.init_request_windows(
number_of_states, max(window_sizes))
state['estimate_windows'] = estimation.init_deque_structure(
window_sizes, number_of_states)
state['variances'] = estimation.init_variances(
window_sizes, number_of_states)
state['acceptable_variances'] = estimation.init_variances(
window_sizes, number_of_states)
for i, current_state in enumerate(
utilization_to_states(state_config, utilization)):
state['request_windows'] = estimation.update_request_windows(
state['request_windows'],
previous_state,
current_state)
state['estimate_windows'] = estimation.update_estimate_windows(
state['estimate_windows'],
state['request_windows'],
previous_state)
state['variances'] = estimation.update_variances(
state['variances'],
state['estimate_windows'],
previous_state)
state['acceptable_variances'] = estimation.update_acceptable_variances(
state['acceptable_variances'],
state['estimate_windows'],
previous_state)
previous_state = current_state
selected_windows = estimation.select_window(
state['variances'],
state['acceptable_variances'],
window_sizes)
p = estimation.select_best_estimates(
state['estimate_windows'],
selected_windows)
# These two are saved for testing purposes
state['selected_windows'] = selected_windows
state['p'] = p
state_vector = build_state_vector(state_config, utilization)
current_state = get_current_state(state_vector)
state['previous_state'] = current_state
state_n = len(state_config)
# if utilization_length > state['time_in_states'] + 1:
# for s in utilization_to_states(
# state_config,
# utilization[-(utilization_length -
# state['time_in_states']):]):
# state['time_in_states'] += 1
# if s == state_n:
# state['time_in_state_n'] += 1
# else:
state['time_in_states'] += 1
if current_state == state_n:
state['time_in_state_n'] += 1
LOG.debug('MHOD utilization:' + str(utilization))
LOG.debug('MHOD time_in_states:' + str(state['time_in_states']))
LOG.debug('MHOD time_in_state_n:' + str(state['time_in_state_n']))
LOG.debug('MHOD p:' + str(p))
LOG.debug('MHOD current_state:' + str(current_state))
LOG.debug('MHOD p[current_state]:' + str(p[current_state]))
if utilization_length >= learning_steps:
if current_state == state_n and p[state_n][state_n] > 0:
# if p[current_state][state_n] > 0:
policy = bruteforce.optimize(
bruteforce_step, 1.0, otf, (migration_time / time_step), ls, p,
state_vector, state['time_in_states'],
state['time_in_state_n'])
# This is saved for testing purposes
state['policy'] = policy
LOG.debug('MHOD policy:' + str(policy))
command = issue_command_deterministic(policy)
LOG.debug('MHOD command:' + str(command))
return command, state
return False, state
def mhod(state_config, otf, window_sizes, bruteforce_step, learning_steps,
time_step, migration_time, utilization, state):
"""The MHOD algorithm returning whether the host is overloaded.
:param state_config: The state configuration.
:param otf: The OTF parameter.
:param window_sizes: A list of window sizes.
:param bruteforce_step: The step of the bruteforce algorithm.
:param time_step: The length of the simulation time step in seconds.
:param migration_time: The VM migration time in time seconds.
:param utilization: The history of the host's CPU utilization.
:param state: The state of the algorithm.
:return: The updated state and decision of the algorithm.
"""
utilization_length = len(utilization)
# if utilization_length == state['time_in_states'] and \
# utilization == state['previous_utilization']:
# # No new utilization values
# return False, state
number_of_states = len(state_config) + 1
previous_state = 0
# state['previous_utilization'] = utilization
state['request_windows'] = estimation.init_request_windows(
number_of_states, max(window_sizes))
state['estimate_windows'] = estimation.init_deque_structure(
window_sizes, number_of_states)
state['variances'] = estimation.init_variances(window_sizes,
number_of_states)
state['acceptable_variances'] = estimation.init_variances(
window_sizes, number_of_states)
for i, current_state in enumerate(
utilization_to_states(state_config, utilization)):
state['request_windows'] = estimation.update_request_windows(
state['request_windows'], previous_state, current_state)
state['estimate_windows'] = estimation.update_estimate_windows(
state['estimate_windows'], state['request_windows'],
previous_state)
state['variances'] = estimation.update_variances(
state['variances'], state['estimate_windows'], previous_state)
state['acceptable_variances'] = estimation.update_acceptable_variances(
state['acceptable_variances'], state['estimate_windows'],
previous_state)
previous_state = current_state
selected_windows = estimation.select_window(state['variances'],
state['acceptable_variances'],
window_sizes)
p = estimation.select_best_estimates(state['estimate_windows'],
selected_windows)
# These two are saved for testing purposes
state['selected_windows'] = selected_windows
state['p'] = p
state_vector = build_state_vector(state_config, utilization)
current_state = get_current_state(state_vector)
state['previous_state'] = current_state
state_n = len(state_config)
# if utilization_length > state['time_in_states'] + 1:
# for s in utilization_to_states(
# state_config,
# utilization[-(utilization_length -
# state['time_in_states']):]):
# state['time_in_states'] += 1
# if s == state_n:
# state['time_in_state_n'] += 1
# else:
state['time_in_states'] += 1
if current_state == state_n:
state['time_in_state_n'] += 1
LOG.debug('MHOD utilization:' + str(utilization))
LOG.debug('MHOD time_in_states:' + str(state['time_in_states']))
LOG.debug('MHOD time_in_state_n:' + str(state['time_in_state_n']))
LOG.debug('MHOD p:' + str(p))
LOG.debug('MHOD current_state:' + str(current_state))
LOG.debug('MHOD p[current_state]:' + str(p[current_state]))
if utilization_length >= learning_steps:
if current_state == state_n and p[state_n][state_n] > 0:
# if p[current_state][state_n] > 0:
policy = bruteforce.optimize(bruteforce_step, 1.0, otf,
(migration_time / time_step), ls, p,
state_vector, state['time_in_states'],
state['time_in_state_n'])
# This is saved for testing purposes
state['policy'] = policy
LOG.debug('MHOD policy:' + str(policy))
command = issue_command_deterministic(policy)
LOG.debug('MHOD command:' + str(command))
return command, state
return False, state