x/terracotta
Code Issues Proposed changes

Added mhod_factory

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This commit is contained in:
Anton Beloglazov 2012-10-20 15:39:48 +11:00
parent bdcb0db0a2
commit 4027f676c0
1 changed files with 47 additions and 16 deletions
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63
neat/locals/overload/mhod/core.py
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@ -26,6 +26,38 @@ import logging
log = logging.getLogger(__name__) log = logging.getLogger(__name__)
@contract
def mhod_factory(time_step, migration_time, params):
""" Creates the MHOD algorithm.
:param time_step: The length of the simulation time step in seconds.
:type time_step: int,>=0
:param migration_time: The VM migration time in time seconds.
:type migration_time: float,>=0
:param params: A dictionary containing the algorithm's parameters.
:type params: dict(str: *)
:return: A function implementing the MHOD algorithm.
:rtype: function
"""
def mhod_wrapper(utilization, state=None):
if not state:
state = init_state(params['window_sizes'],
len(params['state_config']) + 1)
return mhod(params['state_config'],
params['otf'],
params['window_sizes'],
params['bruteforce_step'],
params['learning_steps'],
time_step,
migration_time,
utilization,
state)
return mhod_wrapper
@contract @contract
def init_state(window_sizes, number_of_states): def init_state(window_sizes, number_of_states):
""" Initialize the state dictionary of the MHOD algorithm. """ Initialize the state dictionary of the MHOD algorithm.
@ -39,22 +71,21 @@ def init_state(window_sizes, number_of_states):
:return: The initialization state dictionary. :return: The initialization state dictionary.
:rtype: dict(str: *) :rtype: dict(str: *)
""" """
state = {} return {
state['previous_state'] = 0 'previous_state': 0,
state['request_windows'] = estimation.init_request_windows( 'request_windows': estimation.init_request_windows(
number_of_states) number_of_states),
state['estimate_windows'] = estimation.init_deque_structure( 'estimate_windows': estimation.init_deque_structure(
window_sizes, number_of_states) window_sizes, number_of_states),
state['variances'] = estimation.init_variances( 'variances': estimation.init_variances(
window_sizes, number_of_states) window_sizes, number_of_states),
state['acceptable_variances'] = estimation.init_variances( 'acceptable_variances': estimation.init_variances(
window_sizes, number_of_states) window_sizes, number_of_states)}
return state
@contract @contract
def execute(state_config, otf, window_sizes, bruteforce_step, def mhod(state_config, otf, window_sizes, bruteforce_step, learning_steps,
time_step, migration_time, utilization, state): time_step, migration_time, utilization, state):
""" The MHOD algorithm returning whether the host is overloaded. """ The MHOD algorithm returning whether the host is overloaded.
:param state_config: The state configuration. :param state_config: The state configuration.
@ -115,7 +146,7 @@ def execute(state_config, otf, window_sizes, bruteforce_step,
state['previous_state']) state['previous_state'])
state['previous_state'] = state state['previous_state'] = state
if len(utilization) >= 30: if len(utilization) >= learning_steps:
state_history = utilization_to_states(state_config, utilization) state_history = utilization_to_states(state_config, utilization)
time_in_states = total_time time_in_states = total_time
time_in_state_n = get_time_in_state_n(state_config, state_history) time_in_state_n = get_time_in_state_n(state_config, state_history)
@ -124,8 +155,8 @@ def execute(state_config, otf, window_sizes, bruteforce_step,
policy = bruteforce.optimize( policy = bruteforce.optimize(
step, 1.0, otf, (migration_time / time_step), ls, step, 1.0, otf, (migration_time / time_step), ls,
p, state_vector, time_in_states, time_in_state_n) p, state_vector, time_in_states, time_in_state_n)
return issue_command_deterministic(policy) return issue_command_deterministic(policy), state
return false return False, state
@contract @contract