class KubejobsController(Controller):
def __init__(self, application_id, parameters):
self.validate(parameters["control_parameters"])
self.logger = ScalingLog(
"diff.controller.log", "controller.log", application_id)
self.application_id = application_id
parameters.update({"app_id": application_id})
# read scaling parameters
self.check_interval = \
parameters["control_parameters"]["check_interval"]
# We use a lock here to prevent race conditions when stopping the
# controller
self.running = True
self.running_lock = threading.RLock()
# The alarm here is responsible for deciding whether to scale up or
# down, or even do nothing
self.alarm = KubeJobs(parameters)
def start_application_scaling(self):
run = True
self.logger.log("Start to control resources")
while run:
self.logger.log("Monitoring application: %s" %
self.application_id)
# Call the alarm to check the application
self.alarm.check_application_state()
# Wait some time
time.sleep(float(self.check_interval))
with self.running_lock:
run = self.running
def stop_application_scaling(self):
with self.running_lock:
self.running = False
def status(self):
return self.alarm.status()
def validate(self, data):
data_model = {
"actuator": six.string_types,
"check_interval": int,
"metric_source": six.string_types,
"schedule_strategy": six.string_types
}
for key in data_model:
if (key not in data):
raise ex.BadRequestException(
"Variable \"{}\" is missing".format(key))
if (not isinstance(data[key], data_model[key])):
raise ex.BadRequestException(
"\"{}\" has unexpected variable type: {}. Was expecting {}"
.format(key, type(data[key]), data_model[key]))
def __init__(self, application_id, plugin_info):
self.logger = ScalingLog("diff.controller.log", "controller.log",
application_id)
self.application_id = application_id
self.instances = plugin_info["instances"]
self.check_interval = plugin_info["check_interval"]
self.trigger_down = plugin_info["trigger_down"]
self.trigger_up = plugin_info["trigger_up"]
self.min_cap = plugin_info["min_cap"]
self.max_cap = plugin_info["max_cap"]
self.actuation_size = plugin_info["actuation_size"]
self.metric_rounding = plugin_info["metric_rounding"]
self.actuator_type = plugin_info["actuator"]
self.metric_source_type = plugin_info["metric_source"]
""" We use a lock here to prevent race conditions
when stopping the controller """
self.running = True
self.running_lock = threading.RLock()
# Gets a new metric source plugin using the given name
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, plugin_info)
# Gets a new actuator plugin using the given name
actuator = ActuatorBuilder().get_actuator(self.actuator_type)
""" The alarm here is responsible for deciding whether to scale up
or down, or even do nothing """
self.alarm = GenericAlarm(actuator, metric_source, self.trigger_down,
self.trigger_up, self.min_cap, self.max_cap,
self.actuation_size, self.metric_rounding,
application_id, self.instances)
def __init__(self, actuator, metric_source, trigger_down, trigger_up,
min_cap, max_cap, metric_rounding, heuristic_options,
application_id, instances):
# TODO: Check parameters
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_cap = min_cap
self.max_cap = max_cap
self.metric_rounding = metric_rounding
self.heuristic_options = heuristic_options
self.application_id = application_id
self.instances = instances
self.logger = ScalingLog(
"%s.proportional.alarm.log" % (application_id),
"controller.log", application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (application_id),
"cap.log",
application_id)
self.last_progress_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z",
'%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def __init__(self, application_id, parameters):
self.logger = ScalingLog(
"diff.controller.log", "controller.log", application_id)
scaling_parameters = parameters["control_parameters"]
self.application_id = application_id
parameters.update({"app_id": application_id})
# read scaling parameters
self.check_interval = scaling_parameters["check_interval"]
self.trigger_down = scaling_parameters["trigger_down"]
self.trigger_up = scaling_parameters["trigger_up"]
self.min_cap = scaling_parameters["min_rep"]
self.max_cap = scaling_parameters["max_rep"]
self.actuation_size = scaling_parameters["actuation_size"]
# The actuator plugin name
self.actuator_type = scaling_parameters["actuator"]
# The metric source plugin name
self.metric_source_type = scaling_parameters["metric_source"]
# We use a lock here to prevent race conditions when stopping the
# controller
self.running = True
self.running_lock = threading.RLock()
# Gets a new metric source plugin using the given name
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, parameters)
# Gets a new actuator plugin using the given name
actuator = ActuatorBuilder().get_actuator(self.actuator_type,
parameters=parameters)
# The alarm here is responsible for deciding whether to scale up or
# down, or even do nothing
self.alarm = KubeJobs(actuator, metric_source,
self.trigger_down, self.trigger_up,
self.min_cap, self.max_cap, self.actuation_size,
application_id)
def __init__(self, actuator, metric_source, trigger_down, trigger_up,
min_cap, max_cap, actuation_size, metric_rounding,
application_id, instances):
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.metric_rounding = metric_rounding
self.application_id = application_id
self.instances = instances
self.load_balancer_url = api.load_balancer_url
self.logger = ScalingLog("%s.generic.alarm.log" % (application_id),
"controller.log",
application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (application_id),
"cap.log",
application_id)
self.last_error = ""
self.last_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z",
'%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def __init__(self, application_id, plugin_info):
self.logger = ScalingLog("proportional_derivative.controller.log",
"controller.log", application_id)
plugin_info = plugin_info["plugin_info"]
self.application_id = application_id
self.instances = plugin_info["instances"]
self.check_interval = plugin_info["check_interval"]
self.trigger_down = plugin_info["trigger_down"]
self.trigger_up = plugin_info["trigger_up"]
self.min_cap = plugin_info["min_cap"]
self.max_cap = plugin_info["max_cap"]
self.metric_rounding = plugin_info["metric_rounding"]
self.actuator_type = plugin_info["actuator"]
self.metric_source_type = plugin_info["metric_source"]
self.heuristic_options = plugin_info["heuristic_options"]
self.running = True
self.running_lock = threading.RLock()
# Gets a new metric source plugin using the given name
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, plugin_info)
# Gets a new actuator plugin using the given name
actuator = ActuatorBuilder().get_actuator(self.actuator_type,
plugin_info)
""" The alarm here is responsible for deciding whether to
scale up or down, or even do nothing """
self.alarm = ProportionalDerivativeAlarm(
actuator, metric_source, self.trigger_down, self.trigger_up,
self.min_cap, self.max_cap, self.metric_rounding,
self.heuristic_options, application_id, self.instances)
def __init__(self, app_id, plugin_info):
self.logger = ScalingLog("pid.controller.log", "controller.log",
app_id)
self.app_id = app_id
self.instances = plugin_info["instances"]
self.check_interval = plugin_info["check_interval"]
self.trigger_down = plugin_info["trigger_down"]
self.trigger_up = plugin_info["trigger_up"]
self.min_cap = plugin_info["min_cap"]
self.max_cap = plugin_info["max_cap"]
self.metric_rounding = plugin_info["metric_rounding"]
self.actuator_type = plugin_info["actuator"]
self.metric_source_type = plugin_info["metric_source"]
self.heuristic_options = plugin_info["heuristic_options"]
self.running = True
self.running_lock = threading.RLock()
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, plugin_info)
actuator = ActuatorBuilder().get_actuator(self.actuator_type)
self.alarm = PIDAlarm(actuator, metric_source, self.trigger_down,
self.trigger_up, self.min_cap, self.max_cap,
self.metric_rounding, self.heuristic_options,
self.app_id, self.instances)
def __init__(self, actuator, metric_source, actuator_metric, trigger_down,
trigger_up, min_quota, max_quota, application_id):
# TODO: Check parameters
self.metric_source = metric_source
self.actuator = actuator
self.actuator_metric = actuator_metric
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_quota = min_quota
self.max_quota = max_quota
self.application_id = application_id
self.logger = ScalingLog(
"%s.vertical.alarm.log" %
(application_id),
"controller.log",
application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (
application_id), "cap.log", application_id)
self.last_progress_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z", '%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
class KubejobsController(Controller):
def __init__(self, application_id, parameters):
self.logger = ScalingLog(
"diff.controller.log", "controller.log", application_id)
scaling_parameters = parameters["control_parameters"]
self.application_id = application_id
parameters.update({"app_id": application_id})
# read scaling parameters
self.check_interval = scaling_parameters["check_interval"]
self.trigger_down = scaling_parameters["trigger_down"]
self.trigger_up = scaling_parameters["trigger_up"]
self.min_cap = scaling_parameters["min_rep"]
self.max_cap = scaling_parameters["max_rep"]
self.actuation_size = scaling_parameters["actuation_size"]
# The actuator plugin name
self.actuator_type = scaling_parameters["actuator"]
# The metric source plugin name
self.metric_source_type = scaling_parameters["metric_source"]
# We use a lock here to prevent race conditions when stopping the controller
self.running = True
self.running_lock = threading.RLock()
# Gets a new metric source plugin using the given name
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, parameters)
# Gets a new actuator plugin using the given name
actuator = ActuatorBuilder().get_actuator(self.actuator_type,
parameters=parameters)
# The alarm here is responsible for deciding whether to scale up or down, or even do nothing
self.alarm = KubeJobs(actuator, metric_source,
self.trigger_down, self.trigger_up,
self.min_cap, self.max_cap, self.actuation_size,
application_id)
def start_application_scaling(self):
run = True
print "Start to control resources"
while run:
self.logger.log("Monitoring application: %s" %
self.application_id)
# Call the alarm to check the application
self.alarm.check_application_state()
# Wait some time
time.sleep(float(self.check_interval))
with self.running_lock:
run = self.running
def stop_application_scaling(self):
with self.running_lock:
self.running = False
def status(self):
return self.alarm.status()
def __init__(self, data):
self.validate(data)
self.logger = ScalingLog("default_scheduler.log", "scheduler.log")
self.trigger_down = data.get("trigger_down")
self.trigger_up = data.get("trigger_up")
self.max_cap = data.get("max_rep")
self.min_cap = data.get("min_rep")
self.actuation_size = data.get("actuation_size")
def __init__(self, application_id, parameters):
self.validate(parameters["control_parameters"])
self.logger = ScalingLog(
"diff.controller.log", "controller.log", application_id)
self.application_id = application_id
parameters.update({"app_id": application_id})
# read scaling parameters
self.check_interval = \
parameters["control_parameters"]["check_interval"]
# We use a lock here to prevent race conditions when stopping the
# controller
self.running = True
self.running_lock = threading.RLock()
# The alarm here is responsible for deciding whether to scale up or
# down, or even do nothing
self.alarm = KubeJobs(parameters)
def __init__(self, data):
# TODO: Check parameters
scaling_parameters = data["control_parameters"]
self.metric_source = MetricSourceBuilder().\
get_metric_source(scaling_parameters.get('metric_source'), data)
self.app_id = data.get('app_id')
scaling_parameters.update({'app_id': self.app_id})
self.scheduler = self.setup_scheduler(scaling_parameters)
self.actuator = self.setup_actuator(scaling_parameters)
self.logger = ScalingLog("%s.generic.alarm.log" % (self.app_id),
"controller.log", self.app_id)
self.cap_logger = ScalingLog("%s.cap.log" % (self.app_id),
"cap.log", self.app_id)
self.last_progress_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z", '%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def __init__(self, data):
self.validate(data)
self.logger = ScalingLog("pid_scheduler.log", "scheduler.log")
heuristic_options = data.get('heuristic_options')
self.max_rep = data.get('max_rep')
self.min_rep = data.get('min_rep')
self.proportional_gain = heuristic_options["proportional_gain"]
self.derivative_gain = heuristic_options["derivative_gain"]
self.integral_gain = heuristic_options["integral_gain"]
self.last_error = None
self.integrated_error = 0
class PIDController(Controller):
def __init__(self, app_id, plugin_info):
self.logger = ScalingLog("pid.controller.log", "controller.log",
app_id)
self.app_id = app_id
self.instances = plugin_info["instances"]
self.check_interval = plugin_info["check_interval"]
self.trigger_down = plugin_info["trigger_down"]
self.trigger_up = plugin_info["trigger_up"]
self.min_cap = plugin_info["min_cap"]
self.max_cap = plugin_info["max_cap"]
self.metric_rounding = plugin_info["metric_rounding"]
self.actuator_type = plugin_info["actuator"]
self.metric_source_type = plugin_info["metric_source"]
self.heuristic_options = plugin_info["heuristic_options"]
self.running = True
self.running_lock = threading.RLock()
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, plugin_info)
actuator = ActuatorBuilder().get_actuator(self.actuator_type)
self.alarm = PIDAlarm(actuator, metric_source, self.trigger_down,
self.trigger_up, self.min_cap, self.max_cap,
self.metric_rounding, self.heuristic_options,
self.app_id, self.instances)
def start_application_scaling(self):
run = True
while run:
self.logger.log("Monitoring application: %s" % (self.app_id))
try:
self.alarm.check_application_state()
except MetricNotFoundException:
self.logger.log("No metrics available")
print "No metrics avaliable"
except Exception as e:
self.logger.log(str(e))
print "Unknown " + str(e)
# Wait some time
time.sleep(float(self.check_interval))
with self.running_lock:
run = self.running
def stop_application_scaling(self):
with self.running_lock:
self.running = False
def status(self):
return self.alarm.status()
class KubeJobs:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, data):
# TODO: Check parameters
scaling_parameters = data["control_parameters"]
self.metric_source = MetricSourceBuilder().\
get_metric_source(scaling_parameters.get('metric_source'), data)
self.app_id = data.get('app_id')
scaling_parameters.update({'app_id': self.app_id})
self.scheduler = self.setup_scheduler(scaling_parameters)
self.actuator = self.setup_actuator(scaling_parameters)
self.logger = ScalingLog("%s.generic.alarm.log" % (self.app_id),
"controller.log", self.app_id)
self.cap_logger = ScalingLog("%s.cap.log" % (self.app_id),
"cap.log", self.app_id)
self.last_progress_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z", '%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def setup_scheduler(self, parameters):
strategy = parameters.get('schedule_strategy')
if strategy == "default":
return DefaultScheduler(parameters)
elif strategy == "pid":
return PidScheduler(parameters)
def setup_actuator(self, parameters):
actuator = parameters.get('actuator')
return ActuatorBuilder().get_actuator(actuator,
parameters)
def check_application_state(self):
"""
Checks the application progress by getting progress metrics from a
metric source, checks if the metrics are new
and tries to modify the
amount of allocated resources if necessary.
"""
# TODO: Check parameters
progress_error_timestamp, progress_error = \
self._get_progress_error(self.app_id)
self.last_action = "Progress error-[%s]-%f" % \
(str(progress_error_timestamp), progress_error)
if self._check_measurements_are_new(progress_error_timestamp):
self._scale(progress_error)
if self.cap != -1:
self.cap_logger.log("%.0f|%s|%s" % (time.time(),
str(self.app_id), str(self.cap)))
self.last_progress_error_timestamp = progress_error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log(self.last_action)
def _get_progress_error(self, app_id):
self.last_action = "Getting progress error"
self.logger.log(self.last_action)
progress_error_measurement = self.metric_source.get_most_recent_value(
app_id)
progress_error_timestamp = progress_error_measurement[0]
progress_error = progress_error_measurement[1]
return progress_error_timestamp, progress_error
def _scale(self, progress_error):
last_replicas = self.actuator.get_number_of_replicas()
info = {'last_replicas': last_replicas,
'progress_error': progress_error}
new_resource_allocation = self.scheduler.scale(info)
if new_resource_allocation is not None:
self.logger.log("Scaling from %d to %d" %
(last_replicas, new_resource_allocation))
self.actuator.adjust_resources(new_resource_allocation)
def _check_measurements_are_new(self, progress_error_timestamp):
return self.last_progress_error_timestamp < progress_error_timestamp
def status(self):
return self.last_action
class GenericController(Controller):
def __init__(self, application_id, plugin_info):
self.logger = ScalingLog("diff.controller.log", "controller.log",
application_id)
self.application_id = application_id
self.instances = plugin_info["instances"]
self.check_interval = plugin_info["check_interval"]
self.trigger_down = plugin_info["trigger_down"]
self.trigger_up = plugin_info["trigger_up"]
self.min_cap = plugin_info["min_cap"]
self.max_cap = plugin_info["max_cap"]
self.actuation_size = plugin_info["actuation_size"]
self.metric_rounding = plugin_info["metric_rounding"]
self.actuator_type = plugin_info["actuator"]
self.metric_source_type = plugin_info["metric_source"]
""" We use a lock here to prevent race conditions
when stopping the controller """
self.running = True
self.running_lock = threading.RLock()
# Gets a new metric source plugin using the given name
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, plugin_info)
# Gets a new actuator plugin using the given name
actuator = ActuatorBuilder().get_actuator(self.actuator_type)
""" The alarm here is responsible for deciding whether to scale up
or down, or even do nothing """
self.alarm = GenericAlarm(actuator, metric_source, self.trigger_down,
self.trigger_up, self.min_cap, self.max_cap,
self.actuation_size, self.metric_rounding,
application_id, self.instances)
def start_application_scaling(self):
run = True
while run:
self.logger.log("Monitoring application: %s" %
(self.application_id))
try:
self.alarm.check_application_state()
except MetricNotFoundException:
self.logger.log("No metrics available")
print "No metrics avaliable"
except Exception as e:
self.logger.log(str(e))
print "Unknown " + str(e)
# Wait some time
time.sleep(float(self.check_interval))
with self.running_lock:
run = self.running
def stop_application_scaling(self):
with self.running_lock:
self.running = False
def status(self):
return self.alarm.status()
class GenericAlarm:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, actuator, metric_source, trigger_down, trigger_up,
min_cap, max_cap, actuation_size, metric_rounding,
application_id, instances):
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_cap = min_cap
self.max_cap = max_cap
self.metric_rounding = metric_rounding
self.actuation_size = actuation_size
self.application_id = application_id
self.instances = instances
self.logger = ScalingLog("%s.generic.alarm.log" % (application_id),
"controller.log", application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (application_id),
"cap.log", application_id)
self.last_error = ""
self.last_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z", '%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def check_application_state(self):
""" Checks the application progress by getting progress metrics from
a metric source, checks if the metrics are new and tries to
modify the amount of allocated resources if necessary. """
try:
self.logger.log("Getting progress error")
self.last_action = "getting progress error"
# Get the progress error value and timestamp
error_timestamp, error = self._get_error(self.application_id)
self.last_action = "Progress error-[%s]-%f" % (
str(error_timestamp), error)
self.logger.log(self.last_action)
""" Check if the metric is new by comparing the timestamps of the
current metric and most recent metric """
if self._check_measurements_are_new(error_timestamp):
self._scale_down(error, self.instances)
self._scale_up(error, self.instances)
if self.cap != -1:
self.cap_logger.log(
"%.0f|%s|%s" %
(time.time(), str(self.application_id), str(self.cap)))
self.last_error = error
self.last_error_timestamp = error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log("Could not acquire more recent metrics")
except Exception as e:
# TODO: Check exception type
self.logger.log(str(e))
return
def _scale_down(self, error, instances):
"""
Checks if it is necessary to scale down, according to
the error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If error is positive and its absolute value is too high, scale down
if error > 0 and error >= self.trigger_down:
self.logger.log("Scaling down")
self.last_action = "Getting allocated resources"
# Get current CPU cap
cap = self.actuator.get_allocated_resources_to_cluster(instances)
new_cap = max(cap - self.actuation_size, self.min_cap)
self.logger.log("Scaling from %d to %d" % (cap, new_cap))
self.last_action = "Scaling from %d to %d" % (cap, new_cap)
# Currently, we use the same cap for all the vms
cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(cap_instances)
self.cap = new_cap
def _scale_up(self, error, instances):
"""
Checks if it is necessary to scale up, according to
the error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If error is negative and its absolute value is too high, scale up
if error < 0 and abs(error) >= self.trigger_up:
self.logger.log("Scaling up")
self.last_action = "Getting allocated resources"
# Get current CPU cap
cap = self.actuator.get_allocated_resources_to_cluster(instances)
new_cap = min(cap + self.actuation_size, self.max_cap)
self.logger.log("Scaling from %d to %d" % (cap, new_cap))
self.last_action = "Scaling from %d to %d" % (cap, new_cap)
# Currently, we use the same cap for all the vms
cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(cap_instances)
self.cap = new_cap
def _get_error(self, application_id):
error_measurement = self.metric_source.get_most_recent_value(
GenericAlarm.ERROR_METRIC_NAME, {"application_id": application_id})
error_timestamp = error_measurement[0]
error = error_measurement[1]
error = round(error, self.metric_rounding)
return error_timestamp, error
def _check_measurements_are_new(self, error_timestamp):
return self.last_error_timestamp < error_timestamp
def status(self):
return self.last_action
class ProportionalDerivativeController(Controller):
def __init__(self, application_id, plugin_info):
self.logger = ScalingLog("proportional_derivative.controller.log",
"controller.log", application_id)
plugin_info = plugin_info["plugin_info"]
self.application_id = application_id
self.instances = plugin_info["instances"]
self.check_interval = plugin_info["check_interval"]
self.trigger_down = plugin_info["trigger_down"]
self.trigger_up = plugin_info["trigger_up"]
self.min_cap = plugin_info["min_cap"]
self.max_cap = plugin_info["max_cap"]
self.metric_rounding = plugin_info["metric_rounding"]
self.actuator_type = plugin_info["actuator"]
self.metric_source_type = plugin_info["metric_source"]
self.heuristic_options = plugin_info["heuristic_options"]
self.running = True
self.running_lock = threading.RLock()
# Gets a new metric source plugin using the given name
metric_source = MetricSourceBuilder().get_metric_source(
self.metric_source_type, plugin_info)
# Gets a new actuator plugin using the given name
actuator = ActuatorBuilder().get_actuator(self.actuator_type,
plugin_info)
""" The alarm here is responsible for deciding whether to
scale up or down, or even do nothing """
self.alarm = ProportionalDerivativeAlarm(
actuator, metric_source, self.trigger_down, self.trigger_up,
self.min_cap, self.max_cap, self.metric_rounding,
self.heuristic_options, application_id, self.instances)
def start_application_scaling(self):
run = True
while run:
self.logger.log("Monitoring application: %s" %
(self.application_id))
# Call the alarm to check the application
try:
self.alarm.check_application_state()
except MetricNotFoundException:
self.logger.log("No metrics available")
except Exception as e:
self.logger.log(str(e))
# Wait some time
time.sleep(float(self.check_interval))
with self.running_lock:
run = self.running
def stop_application_scaling(self):
with self.running_lock:
self.running = False
def status(self):
return self.alarm.status()
class GenericAlarm:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, actuator, metric_source, trigger_down, trigger_up,
min_cap, max_cap, actuation_size, metric_rounding,
application_id, instances):
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.metric_rounding = metric_rounding
self.application_id = application_id
self.instances = instances
self.load_balancer_url = api.load_balancer_url
self.logger = ScalingLog("%s.generic.alarm.log" % (application_id),
"controller.log",
application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (application_id),
"cap.log",
application_id)
self.last_error = ""
self.last_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z",
'%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def check_application_state(self):
""" Checks the application progress by getting progress metrics from
a metric source, checks if the metrics are new and tries to
modify the amount of allocated resources if necessary. """
try:
self.logger.log("Getting progress error")
self.last_action = "getting progress error"
# Get the progress error value and timestamp
error_timestamp, error = self._get_error(self.application_id)
self.last_action = "Progress error-[%s]-%f" % (
str(error_timestamp), error)
self.logger.log(self.last_action)
""" Check if the metric is new by comparing the timestamps of the
current metric and most recent metric """
if self._check_measurements_are_new(error_timestamp):
print "TO NO ALARME"
self._scale_down(error, self.instances)
self._scale_up(error, self.instances)
self.last_error = error
self.last_error_timestamp = error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log("Could not acquire more recent metrics")
except Exception as e:
# TODO: Check exception type
self.logger.log(str(e))
return
def _scale_down(self, error, instances):
"""
Checks if it is necessary to scale down, according to
the error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If error is positive and its absolute value is too high, scale down
if error > 0:
print "TO NO SCALE DOWN"
self.logger.log("Scaling down")
self.last_action = "Getting allocated resources"
if error < 0.1:
requests.post(self.load_balancer_url + "/down", json={"vm_number": 1})
elif error < 0.2:
requests.post(self.load_balancer_url + "/down", json={"vm_number": 2})
else:
requests.post(self.load_balancer_url + "/down", json={"vm_number": 3})
def _scale_up(self, error, instances):
"""
Checks if it is necessary to scale up, according to
the error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If error is negative and its absolute value is too high, scale up
if error < 0:
print "TO NO SCALE UP"
self.logger.log("Scaling up")
self.last_action = "Getting allocated resources"
if error > -0.1:
requests.post(self.load_balancer_url + "/up", json={"vm_number": 1})
elif error > -0.2:
requests.post(self.load_balancer_url + "/up",json={"vm_number": 2})
else:
requests.post(self.load_balancer_url+ "/up",json={"vm_number": 3})
def _get_error(self, application_id):
error_measurement = self.metric_source.get_most_recent_value(
GenericAlarm.ERROR_METRIC_NAME,
{"application_id": application_id})
error_timestamp = error_measurement[0]
error = error_measurement[1]
error = round(error, self.metric_rounding)
return error_timestamp, error
def _check_measurements_are_new(self, error_timestamp):
return self.last_error_timestamp < error_timestamp
def status(self):
return self.last_action
class PIDAlarm:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, actuator, metric_source, trigger_down, trigger_up,
min_cap, max_cap, metric_rounding, heuristic_options,
application_id, instances):
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_cap = min_cap
self.max_cap = max_cap
self.metric_rounding = metric_rounding
self.heuristic_options = heuristic_options
self.application_id = application_id
self.instances = instances
self.integrated_error = 0
self.logger = ScalingLog("%s.pid.alarm.log" % (application_id),
"controller.log", application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (
application_id), "cap.log", application_id)
self.last_error = ""
self.last_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z",
'%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def check_application_state(self):
"""
Checks the application progress by getting progress metrics from a
metric source, checks if the metrics are new and tries to modify
the amount of allocated resources if necessary.
"""
self.last_action = "getting progress error"
# Get the progress error value and timestamp
error_timestamp, error = self._get_error(
self.application_id)
self.last_action = "Progress error-[%s]-%f" % (
str(error_timestamp), error)
print self.last_action
# Check if the metric is new by comparing the timestamps
# of the current metric and most recent metric
if self._check_measurements_are_new(error_timestamp):
self._scale(error, self.instances)
if self.cap != -1:
print("%.0f|%s|%s" % (
time.time(), str(self.application_id), str(self.cap)))
self.last_error = error
self.last_error_timestamp = error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log("Could not acquire more recent metrics")
def _scale(self, error, instances):
"""
Checks if it is necessary to scale, according to
the error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
self.logger.log("Scaling down")
self.last_action = "Getting allocated resources"
# Get current CPU cap
cap = self.actuator.get_allocated_resources_to_cluster(instances)
new_cap = self._decide_next_cap(
cap, error, self.heuristic_options)
self.logger.log("Scaling from %d to %d" % (cap, new_cap))
self.last_action = "Scaling from %d to %d" % (cap, new_cap)
# Currently, we use the same cap for all the vms
cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(cap_instances)
self.cap = new_cap
def _get_error(self, application_id):
error_measurement = self.metric_source.get_most_recent_value(
PIDAlarm.ERROR_METRIC_NAME,
{"application_id": application_id}
)
error_timestamp = error_measurement[0]
error = error_measurement[1]
error = round(error, self.metric_rounding)
return error_timestamp, error
def _check_measurements_are_new(self, error_timestamp):
return self.last_error_timestamp < error_timestamp
def _decide_next_cap(self, current_cap, error, heuristic_options):
heuristic = heuristic_options["heuristic_name"]
if heuristic == "error_pid":
return self._error_pid(current_cap, error, heuristic_options)
else:
raise Exception("Unknown heuristic")
def _error_pid(self, current_cap, error, heuristic_options):
"""
Calculates the new cap value using a PID algorithm.
The new cap expression is:
new cap = old cap
- proportional_factor * error
- derivative_factor * (error difference)
- integrative_factor * (integrated_error)
"""
proportional_factor = heuristic_options["proportional_factor"]
derivative_factor = heuristic_options["derivative_factor"]
integrative_factor = heuristic_options["integrative_factor"]
proportional_component = -1 * error * proportional_factor
""" If it is the first call, there is no last_error and the derivative
component value is null """
if self.last_error == "":
derivative_component = 0
else:
derivative_component = -1 * derivative_factor * \
(error - self.last_error)
self.integrated_error += error
integrative_component = -1 * self.integrated_error * integrative_factor
new_cap = current_cap + proportional_component + \
derivative_component + integrative_component
new_cap = max(min(new_cap, self.max_cap), self.min_cap)
return new_cap
class ProportionalAlarm:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, actuator, metric_source, trigger_down, trigger_up,
min_cap, max_cap, metric_rounding, heuristic_options,
application_id, instances):
# TODO: Check parameters
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_cap = min_cap
self.max_cap = max_cap
self.metric_rounding = metric_rounding
self.heuristic_options = heuristic_options
self.application_id = application_id
self.instances = instances
self.logger = ScalingLog(
"%s.proportional.alarm.log" % (application_id),
"controller.log", application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (application_id),
"cap.log",
application_id)
self.last_progress_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z",
'%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def check_application_state(self):
"""
Checks the application progress by getting progress metrics from a
metric source, checks if the metrics are new and tries to modify
the amount of allocated resources if necessary.
"""
# TODO: Check parameters
self.logger.log("Getting progress error")
self.last_action = "getting progress error"
# Get the progress error value and timestamp
progress_error_timestamp, progress_error = self._get_progress_error(
self.application_id)
self.logger.log("Progress error-[%s]-%f" %
(str(progress_error_timestamp), progress_error))
self.last_action = "Progress error-[%s]-%f" % (
str(progress_error_timestamp), progress_error)
""" Check if the metric is new by comparing the timestamps of the
current metric and most recent metric """
if self._check_measurements_are_new(progress_error_timestamp):
self._scale_down(progress_error, self.instances)
self._scale_up(progress_error, self.instances)
if self.cap != -1:
self.cap_logger.log("%.0f|%s|%s" % (
time.time(), str(self.application_id), str(self.cap)))
self.last_progress_error_timestamp = progress_error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log("Could not acquire more recent metrics")
def _scale_down(self, progress_error, instances):
"""
Checks if it is necessary to scale down, according to
the progress_error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If error is positive and its absolute value is too high, scale down
if progress_error > 0 and progress_error >= self.trigger_down:
self.logger.log("Scaling down")
self.last_action = "Getting allocated resources"
# Get current CPU cap
cap = self.actuator.get_allocated_resources_to_cluster(instances)
new_cap = self._decide_next_cap(
cap, progress_error, self.heuristic_options)
self.logger.log("Scaling from %d to %d" % (cap, new_cap))
self.last_action = "Scaling from %d to %d" % (cap, new_cap)
# Currently, we use the same cap for all the vms
cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(cap_instances)
self.cap = new_cap
def _scale_up(self, progress_error, instances):
"""
Checks if it is necessary to scale up, according to
the progress_error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If the error is negative and its absolute value is too high, scale up
if progress_error < 0 and abs(progress_error) >= self.trigger_up:
self.logger.log("Scaling up")
self.last_action = "Getting allocated resources"
# Get current CPU cap
cap = self.actuator.get_allocated_resources_to_cluster(instances)
new_cap = self._decide_next_cap(
cap, progress_error, self.heuristic_options)
self.logger.log("Scaling from %f to %f" % (cap, new_cap))
self.last_action = "Scaling from %d to %d" % (cap, new_cap)
# Currently, we use the same cap for all the vms
cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(cap_instances)
self.cap = new_cap
def _get_progress_error(self, application_id):
progress_error_measurement = self.metric_source.get_most_recent_value(
Proportional_Alarm.ERROR_METRIC_NAME,
{"application_id": application_id})
progress_error_timestamp = progress_error_measurement[0]
progress_error = progress_error_measurement[1]
progress_error = round(progress_error, self.metric_rounding)
return progress_error_timestamp, progress_error
def _check_measurements_are_new(self, progress_error_timestamp):
return self.last_progress_error_timestamp < progress_error_timestamp
def _decide_next_cap(self, current_cap, progress_error, heuristic_options):
heuristic = heuristic_options["heuristic_name"]
if heuristic == "error_proportional":
return self._error_proportional(current_cap, progress_error,
heuristic_options)
elif heuristic == "error_proportional_up_down":
return self._error_proportional_up_down(current_cap,
progress_error,
heuristic_options)
else:
raise Exception("Unknown heuristic")
def _error_proportional(self, current_cap, progress_error,
heuristic_options):
"""
Calculates the new cap value using a proportional algorithm, with
single control parameter.
The new cap expression is:
new cap = old cap + conservative_factor*progress_error
"""
proportional_factor = heuristic_options["proportional_factor"]
actuation_size = abs(progress_error * proportional_factor)
if progress_error < 0:
return min(current_cap + actuation_size, self.max_cap)
else:
return max(current_cap - actuation_size, self.min_cap)
def _error_proportional_up_down(self, current_cap, progress_error,
heuristic_options):
"""
Calculates the new cap value using a proportional algorithm, with
adjust parameters for scaling down and up.
The new cap expression is:
new cap = old cap + factor*progress_error
"""
if progress_error < 0:
factor = heuristic_options["factor_up"]
actuation_size = abs(progress_error * factor)
return min(current_cap + actuation_size, self.max_cap)
else:
factor = heuristic_options["factor_down"]
actuation_size = abs(progress_error * factor)
return max(current_cap - actuation_size, self.min_cap)
def status(self):
return self.last_action
class KubeJobs:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, actuator, metric_source, trigger_down,
trigger_up, min_cap, max_cap, actuation_size, application_id):
# TODO: Check parameters
self.metric_source = metric_source
self.actuator = actuator
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_cap = min_cap
self.max_cap = max_cap
self.actuation_size = actuation_size
self.application_id = application_id
self.logger = ScalingLog("%s.generic.alarm.log" % (application_id), "controller.log",
application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (
application_id), "cap.log", application_id)
self.last_progress_error_timestamp = datetime.datetime.strptime("0001-01-01T00:00:00.0Z",
'%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def check_application_state(self):
"""
Checks the application progress by getting progress metrics from a
metric source, checks if the metrics are new and tries to modify the
amount of allocated resources if necessary.
"""
# TODO: Check parameters
try:
self.logger.log("Getting progress error")
self.last_action = "getting progress error"
# Get the progress error value and timestamp
progress_error_timestamp, progress_error = self._get_progress_error(
self.application_id)
self.logger.log(
"Progress error-[%s]-%f" % (str(progress_error_timestamp), progress_error))
self.last_action = "Progress error-[%s]-%f" % (
str(progress_error_timestamp), progress_error)
# Check if the metric is new by comparing the timestamps of the current metric and most recent metric
if self._check_measurements_are_new(progress_error_timestamp):
self._scale_down(progress_error)
self._scale_up(progress_error)
if self.cap != -1:
self.cap_logger.log("%.0f|%s|%s" % (time.time(),
str(self.application_id), str(self.cap)))
self.last_progress_error_timestamp = progress_error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log("Could not acquire more recent metrics")
except Exception as e:
# TODO: Check exception type
self.logger.log(str(e))
raise e
def _scale_down(self, progress_error):
"""
Checks if it is necessary to scale down, according to
the progress_error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If the error is positive and its absolute value is too high, scale down
if progress_error > 0 and progress_error >= self.trigger_down:
self.logger.log("Scaling down")
self.last_action = "Getting allocated resources"
# Get current CPU cap
replicas = self.actuator.get_number_of_replicas()
new_replicas = max(replicas - self.actuation_size, self.min_cap)
# new_cap = max(cap - self.actuation_size, self.min_cap)
self.logger.log("Scaling from %d to %d" % (replicas, new_replicas))
self.last_action = "Scaling from %d to %d" % (replicas, new_replicas)
# Currently, we use the same cap for all the vms
# cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(new_replicas)
def _scale_up(self, progress_error):
"""
Checks if it is necessary to scale up, according to
the progress_error. If it is, calculates the new CPU cap
value and tries to modify the cap of the vms.
"""
# If the error is negative and its absolute value is too high, scale up
if progress_error < 0 and abs(progress_error) >= self.trigger_up:
self.logger.log("Scaling up")
self.last_action = "Getting allocated resources"
# Get current number of replicas
replicas = self.actuator.get_number_of_replicas()
new_replicas = min(replicas + self.actuation_size, self.max_cap)
self.logger.log("Scaling from %d to %d" % (replicas, new_replicas))
self.last_action = "Scaling from %d to %d" % (replicas, new_replicas)
# Currently, we use the same cap for all the vms
# cap_instances = {instance: new_cap for instance in instances}
# Set the new cap
self.actuator.adjust_resources(new_replicas)
def _get_progress_error(self, application_id):
progress_error_measurement = self.metric_source.get_most_recent_value(application_id)
progress_error_timestamp = progress_error_measurement[0]
progress_error = progress_error_measurement[1]
return progress_error_timestamp, progress_error
def _check_measurements_are_new(self, progress_error_timestamp):
return self.last_progress_error_timestamp < progress_error_timestamp
def status(self):
return self.last_action
class Vertical:
ERROR_METRIC_NAME = "application-progress.error"
def __init__(self, actuator, metric_source, actuator_metric, trigger_down,
trigger_up, min_quota, max_quota, application_id):
# TODO: Check parameters
self.metric_source = metric_source
self.actuator = actuator
self.actuator_metric = actuator_metric
self.trigger_down = trigger_down
self.trigger_up = trigger_up
self.min_quota = min_quota
self.max_quota = max_quota
self.application_id = application_id
self.logger = ScalingLog("%s.vertical.alarm.log" % (application_id),
"controller.log", application_id)
self.cap_logger = ScalingLog("%s.cap.log" % (application_id),
"cap.log", application_id)
self.last_progress_error_timestamp = datetime.datetime.strptime(
"0001-01-01T00:00:00.0Z", '%Y-%m-%dT%H:%M:%S.%fZ')
self.last_action = ""
self.cap = -1
def check_application_state(self):
"""
Checks the application progress by getting progress metrics from a
metric source, checks if the metrics are new and tries to modify the
amount of allocated resources if necessary.
"""
# TODO: Check parameters
try:
self.logger.log("Getting progress error")
self.last_action = "getting progress error"
# Get the progress error value and timestamp
progress_error_timestamp, progress_error = self._get_progress_error(
self.application_id)
self.logger.log("Progress error-[%s]-%f" %
(str(progress_error_timestamp), progress_error))
self.last_action = "Progress error-[%s]-%f" % (
str(progress_error_timestamp), progress_error)
# Check if the metric is new by comparing the timestamps of the current metric and most recent metric
if self._check_measurements_are_new(progress_error_timestamp):
self._scale_down(progress_error)
self._scale_up(progress_error)
if self.cap != -1:
self.cap_logger.log(
"%.0f|%s|%s" %
(time.time(), str(self.application_id), str(self.cap)))
self.last_progress_error_timestamp = progress_error_timestamp
else:
self.last_action += " Could not acquire more recent metrics"
self.logger.log("Could not acquire more recent metrics")
except Exception as e:
# TODO: Check exception type
self.logger.log(str(e))
raise e
def _scale_down(self, progress_error):
"""Scales down the specific resource using an external API.
Arguments:
progress_error {float} -- progress error of the job
"""
# If the error is positive and its absolute value is too high, scale down
if progress_error > 0 and progress_error >= self.trigger_down:
if self.actuator_metric == 'cpu':
self.logger.log("Scaling down")
self.last_action = "Getting allocated resources"
self.logger.log(
"Scaling %s quota from %d / %d" %
(self.actuator_metric, self.max_quota, self.max_quota))
print("Scaling %s from %d / %d" %
(self.actuator_metric, self.max_quota, self.max_quota))
self.set_cpu_quota(self.max_quota)
def _scale_up(self, progress_error):
"""Scales up the specific resource using an external API.
Arguments:
progress_error {float} -- progress error of the job
"""
# If the error is negative and its absolute value is too high, scale up
if progress_error < 0 and abs(progress_error) >= self.trigger_up:
if self.actuator_metric == 'cpu':
self.logger.log("Scaling up")
self.last_action = "Getting allocated resources"
self.logger.log("Scaling from %d / %d" %
(self.min_quota, self.max_quota))
print("Scaling from %d / %d" %
(self.min_quota, self.max_quota))
self.set_cpu_quota(self.min_quota)
def _get_progress_error(self, application_id):
"""Gets the progress error of the job
Arguments:
application_id {string} -- The application identifier
Returns:
[tuple] -- Returns a tuple containing the progress error timestamp and
the current value of the progress error
"""
progress_error_measurement = self.metric_source.get_most_recent_value(
application_id)
progress_error_timestamp = progress_error_measurement[0]
progress_error = progress_error_measurement[1]
return progress_error_timestamp, progress_error
def _check_measurements_are_new(self, progress_error_timestamp):
"""Check if the currently measurements where already computed.
Arguments:
progress_error_timestamp {string} -- Timestamp of the current progress error
Returns:
[boolean] -- 'true' if the measurements are new, 'false' otherwise
"""
return self.last_progress_error_timestamp < progress_error_timestamp
def status(self):
return self.last_action
def set_cpu_quota(self, new_cpu_quota):
"""Sets the CPU quota of the physical machine using a external API
Arguments:
new_cpu_quota {int} -- The new value for the CPU quota of the machine
"""
try:
r = requests.post('http://%s:5000' % (self.actuator.api_address),
data='{\"cpu_quota\":\"' + str(new_cpu_quota) +
'\"}')
except Exception as ex:
print("Error while modifying cpu quota")
print ex.message
raise
