def __init__(self,
database: ModelDatabase,
action_delay: float,
agg_period: float = 20,
model_pull_cycle: float = 180,
metric_file: str = Analyzer.METRIC_FILE):
log.debug('action_delay: %i, agg_period: %i, model_pull_cycle: %i',
action_delay, agg_period, model_pull_cycle)
self.metric_file = metric_file
self.agg_cnt = int(agg_period) / int(action_delay) \
if int(agg_period) % int(action_delay) == 0 else 1
self.counter = 0
self.agg = False
self.container_map = dict()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = [
'time', 'cid', 'name', 'cpu_model', 'vcpu_count', Metric.CYC,
Metric.INST, Metric.L3MISS, Metric.L3OCC, Metric.MB, Metric.CPI,
Metric.L3MPKI, Metric.NF, Metric.UTIL, Metric.MSPKI
]
self.workload_meta = {}
self.analyzer = Analyzer()
if database:
self.database = database
self.model_pull_cycle = model_pull_cycle
self.threshs = {}
self.cycle = 0
def __init__(self,
action_delay: int,
mode_config: str = 'collect',
agg_period: float = 20):
log.debug('action_delay: %i, mode config: %s, agg_period: %i',
action_delay, mode_config, agg_period)
self.mode_config = mode_config
self.agg_cnt = int(agg_period) / int(action_delay) \
if int(agg_period) % int(action_delay) == 0 else 1
self.counter = 0
self.agg = False
self.container_map = dict()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = [
'time', 'cid', 'name', Metric.CYC, Metric.INST, Metric.L3MISS,
Metric.L3OCC, Metric.MB, Metric.CPI, Metric.L3MPKI, Metric.NF,
Metric.UTIL, Metric.MSPKI
]
if mode_config == ContentionDetector.COLLECT_MODE:
self.analyzer = Analyzer()
self.workload_meta = {}
self._init_data_file(Analyzer.UTIL_FILE, self.ucols)
self._init_data_file(Analyzer.METRIC_FILE, self.mcols)
else:
try:
with open(ContentionDetector.WL_META_FILE, 'r') as wlf:
self.analyzer = Analyzer(wlf)
except Exception as e:
log.exception('cannot read workload file - stopped')
raise e
self.analyzer.build_model()
def __init__(self,
action_delay: float,
mode_config: str = 'collect',
agg_period: float = 20,
exclusive_cat: bool = False):
log.debug(
'action_delay: %i, mode config: %s, agg_period: %i, exclusive: %s',
action_delay, mode_config, agg_period, exclusive_cat)
self.mode_config = mode_config
self.exclusive_cat = exclusive_cat
self.agg_cnt = int(agg_period) / int(action_delay) \
if int(agg_period) % int(action_delay) == 0 else 1
self.counter = 0
self.agg = False
self.container_map = dict()
self.bes = set()
self.lcs = set()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = [
'time', 'cid', 'name', Metric.CYC, Metric.INST, Metric.L3MISS,
Metric.L3OCC, Metric.MB, Metric.CPI, Metric.L3MPKI, Metric.NF,
Metric.UTIL, Metric.MSPKI
]
if mode_config == ResourceAllocator.COLLECT_MODE:
self.analyzer = Analyzer()
self.workload_meta = {}
self._init_data_file(Analyzer.UTIL_FILE, self.ucols)
self._init_data_file(Analyzer.METRIC_FILE, self.mcols)
else:
try:
with open(ResourceAllocator.WL_META_FILE, 'r') as wlf:
self.analyzer = Analyzer(wlf)
except Exception as e:
log.exception('cannot read workload file - stopped')
raise e
self.analyzer.build_model()
self.cpuc = CpuCycle(self.analyzer.get_lcutilmax(), 0.5, False)
self.l3c = LlcOccup(self.exclusive_cat)
self.mbc_enabled = True
self.mbc = MemoryBw()
cpuc_controller = NaiveController(self.cpuc, 15)
llc_controller = NaiveController(self.l3c, 4)
mb_controller = NaiveController(self.mbc, 4)
self.controllers = {
ContendedResource.CPUS: cpuc_controller,
ContendedResource.LLC: llc_controller,
ContendedResource.MEMORY_BW: mb_controller
}
def __init__(self,
database: ModelDatabase,
action_delay: float,
agg_period: float = 20,
model_pull_cycle: float = 180,
metric_file: str = Analyzer.METRIC_FILE,
enable_control: bool = True,
exclusive_cat: bool = False):
log.debug(
'action_delay: %i, agg_period: %i, exclusive: %s, model_pull_cycle: %i',
action_delay, agg_period, exclusive_cat, model_pull_cycle)
self.metric_file = metric_file
self.exclusive_cat = exclusive_cat
self.enable_control = enable_control
self.agg_cnt = int(agg_period) / int(action_delay) \
if int(agg_period) % int(action_delay) == 0 else 1
self.counter = 0
self.agg = False
self.container_map = dict()
self.bes = set()
self.lcs = set()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = [
'time', 'cid', 'name', 'cpu_model', 'vcpu_count', Metric.CYC,
Metric.INST, Metric.L3MISS, Metric.L3OCC, Metric.MB, Metric.CPI,
Metric.L3MPKI, Metric.NF, Metric.UTIL, Metric.MSPKI
]
self.workload_meta = {}
self.analyzer = Analyzer()
if database:
self.database = database
self.model_pull_cycle = model_pull_cycle
self.threshs = {}
self.cycle = 0
if enable_control:
self.cpuc = CpuCycle(self.analyzer.get_lcutilmax(), 0.5, False)
self.l3c = LlcOccup(self.exclusive_cat)
self.mbc_enabled = True
self.mbc = MemoryBw()
cpuc_controller = NaiveController(self.cpuc, 15)
llc_controller = NaiveController(self.l3c, 4)
mb_controller = NaiveController(self.mbc, 4)
self.controllers = {
ContendedResource.CPUS: cpuc_controller,
ContendedResource.LLC: llc_controller,
ContendedResource.MEMORY_BW: mb_controller
}
def __init__(self, mode_config: str = 'collect'):
log.debug('Mode config: %s', mode_config)
self.mode_config = mode_config
self.container_map = dict()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = [
'time', 'cid', 'name', Metric.CYC, Metric.INST, Metric.L3MISS,
Metric.L3OCC, Metric.MB, Metric.CPI, Metric.L3MPKI, Metric.NF,
Metric.UTIL
]
if mode_config == ContentionDetector.COLLECT_MODE:
self.analyzer = Analyzer()
self.workload_meta = {}
self._init_data_file(Analyzer.UTIL_FILE, self.ucols)
self._init_data_file(Analyzer.METRIC_FILE, self.mcols)
else:
try:
with open(ContentionDetector.WL_META_FILE, 'r') as wlf:
self.analyzer = Analyzer(wlf)
except Exception as e:
log.exception('cannot read workload file - stopped')
raise e
self.analyzer.build_model()
class ContentionDetector(detectors.AnomalyDetector):
COLLECT_MODE = 'collect'
DETECT_MODE = 'detect'
WL_META_FILE = 'workload.json'
def __init__(self,
action_delay: int,
mode_config: str = 'collect',
agg_period: float = 20):
log.debug('action_delay: %i, mode config: %s, agg_period: %i',
action_delay, mode_config, agg_period)
self.mode_config = mode_config
self.agg_cnt = int(agg_period) / int(action_delay) \
if int(agg_period) % int(action_delay) == 0 else 1
self.counter = 0
self.agg = False
self.container_map = dict()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = [
'time', 'cid', 'name', Metric.CYC, Metric.INST, Metric.L3MISS,
Metric.L3OCC, Metric.MB, Metric.CPI, Metric.L3MPKI, Metric.NF,
Metric.UTIL, Metric.MSPKI
]
if mode_config == ContentionDetector.COLLECT_MODE:
self.analyzer = Analyzer()
self.workload_meta = {}
self._init_data_file(Analyzer.UTIL_FILE, self.ucols)
self._init_data_file(Analyzer.METRIC_FILE, self.mcols)
else:
try:
with open(ContentionDetector.WL_META_FILE, 'r') as wlf:
self.analyzer = Analyzer(wlf)
except Exception as e:
log.exception('cannot read workload file - stopped')
raise e
self.analyzer.build_model()
def _init_data_file(self, data_file, cols):
headline = None
try:
with open(data_file, 'r') as dtf:
headline = dtf.readline()
except Exception:
log.debug('cannot open %r for reading - ignore', data_file)
if headline != ','.join(cols) + '\n':
with open(data_file, 'w') as dtf:
dtf.write(','.join(cols) + '\n')
def _detect_contenders(self, con: Container, resource: ContendedResource):
contenders = []
if resource == ContendedResource.UNKN:
return contenders
resource_delta_max = -np.Inf
contender_id = None
for cid, container in self.container_map.items():
delta = 0
if con.cid == container.cid:
continue
if resource == ContendedResource.LLC:
delta = container.get_llcoccupany_delta()
elif resource == ContendedResource.MEMORY_BW:
delta = container.get_latest_mbt()
elif resource == ContendedResource.TDP:
delta = container.get_freq_delta()
if delta > 0 and delta > resource_delta_max:
resource_delta_max = delta
contender_id = container.cid
if contender_id:
contenders.append(contender_id)
return contenders
def _append_anomaly(self, anomalies, res, cid, contenders, wca_metrics):
anomaly = ContentionAnomaly(resource=res,
contended_task_id=cid,
contending_task_ids=contenders,
metrics=wca_metrics)
anomalies.append(anomaly)
def _detect_one_task(self, con: Container, app: str):
anomalies = []
if not con.get_metrics():
return anomalies
analyzer = self.analyzer
cid = con.cid
if app in analyzer.threshold:
thresh = analyzer.get_thresh(app)
contends, wca_metrics = con.contention_detect(thresh)
log.debug('cid=%r contends=%r', cid, contends)
log.debug('cid=%r threshold metrics=%r', cid, wca_metrics)
for contend in contends:
contenders = self._detect_contenders(con, contend)
self._append_anomaly(anomalies, contend, cid, contenders,
wca_metrics)
thresh_tdp = analyzer.get_tdp_thresh(app)
tdp_contend, wca_metrics = con.tdp_contention_detect(thresh_tdp)
if tdp_contend:
contenders = self._detect_contenders(con, tdp_contend)
self._append_anomaly(anomalies, tdp_contend, cid, contenders,
wca_metrics)
return anomalies
def _get_container_from_taskid(self, cid):
if cid in self.container_map:
container = self.container_map[cid]
else:
container = Container(cid)
self.container_map[cid] = container
return container
def _remove_finished_tasks(self, cidset: set):
for cid in self.container_map.copy():
if cid not in cidset:
del self.container_map[cid]
def _cid_to_app(self, cid, tasks_labels: TasksLabels):
"""
Maps container id to a string key identifying statistical model instance.
"""
if 'application' in tasks_labels[cid] and\
'application_version_name' in tasks_labels[cid]:
return tasks_labels[cid]['application'] + '.' +\
tasks_labels[cid]['application_version_name']
else:
log.debug(
'no label "application" or "application_version_name" '
'passed to detect function by wca for container: {}'.format(
cid))
return None
def _is_be_app(self, cid, tasks_labels: TasksLabels):
if 'type' in tasks_labels[cid] and tasks_labels[cid][
'type'] == 'best_efforts':
return True
return False
def _get_headroom_metrics(self, assign_cpus, lcutil, sysutil):
util_max = self.analyzer.get_lcutilmax()
if util_max < lcutil:
self.analyzer.update_lcutilmax(lcutil)
util_max = lcutil
capacity = assign_cpus * 100
return [
WCAMetric(name=Metric.LCCAPACITY, value=capacity),
WCAMetric(name=Metric.LCMAX, value=util_max),
WCAMetric(name=Metric.SYSUTIL, value=sysutil)
]
def _get_threshold_metrics(self):
"""Encode threshold objects as WCA metrics.
In contrast to *_threshold metrics from Container,
all utilization partitions are exposed for all workloads.
"""
metrics = []
# Only when debugging is enabled.
if log.getEffectiveLevel() == logging.DEBUG:
for cid, threshold in self.analyzer.threshold.items():
if cid == 'lcutilmax':
metrics.append(
WCAMetric(name='threshold_lcutilmax', value=threshold))
continue
if 'tdp' in threshold and 'bar' in threshold['tdp']:
metrics.extend([
WCAMetric(name='threshold_tdp_bar',
value=threshold['tdp']['bar'],
labels=dict(cid=cid)),
WCAMetric(name='threshold_tdp_util',
value=threshold['tdp']['util'],
labels=dict(cid=cid)),
])
if 'thresh' in threshold:
for d in threshold['thresh']:
metrics.extend([
WCAMetric(name='threshold_cpi',
labels=dict(start=str(
int(d['util_start'])),
end=str(int(d['util_end'])),
cid=cid),
value=d['cpi']),
WCAMetric(name='threshold_mpki',
labels=dict(start=str(
int(d['util_start'])),
end=str(int(d['util_end'])),
cid=cid),
value=(d['mpki'])),
WCAMetric(name='threshold_mb',
labels=dict(start=str(
int(d['util_start'])),
end=str(int(d['util_end'])),
cid=cid),
value=(d['mb'])),
])
return metrics
def _record_utils(self, time, utils):
row = [str(time), '', 'lcs']
for i in range(3, len(self.ucols)):
row.append(str(utils))
with open(Analyzer.UTIL_FILE, 'a') as utilf:
utilf.write(','.join(row) + '\n')
def _record_metrics(self, time, cid, name, metrics):
row = [str(time), cid, name]
for i in range(3, len(self.mcols)):
row.append(str(metrics[self.mcols[i]]))
with open(Analyzer.METRIC_FILE, 'a') as metricf:
metricf.write(','.join(row) + '\n')
def _update_workload_meta(self):
with open(ContentionDetector.WL_META_FILE, 'w') as wlf:
wlf.write(json.dumps(self.workload_meta))
def _get_task_resources(self, tasks_resources: TasksResources,
tasks_labels: TasksLabels):
assigned_cpus = 0
cidset = set()
for cid, resources in tasks_resources.items():
cidset.add(cid)
if not self._is_be_app(cid, tasks_labels):
assigned_cpus += resources['cpus']
if self.mode_config == ContentionDetector.COLLECT_MODE:
app = self._cid_to_app(cid, tasks_labels)
if app:
self.workload_meta[app] = resources
if self.mode_config == ContentionDetector.COLLECT_MODE:
self._update_workload_meta()
self._remove_finished_tasks(cidset)
return assigned_cpus
def _process_measurements(self, tasks_measurements: TasksMeasurements,
tasks_labels: TasksLabels,
metric_list: List[wcaMetric], timestamp: float,
assigned_cpus: float):
sysutil = 0
lcutil = 0
for cid, measurements in tasks_measurements.items():
app = self._cid_to_app(cid, tasks_labels)
container = self._get_container_from_taskid(cid)
container.update_measurement(timestamp, measurements, self.agg)
if not self._is_be_app(cid, tasks_labels):
lcutil += container.util
sysutil += container.util
if self.agg:
metrics = container.get_metrics()
log.debug('cid=%r container metrics=%r', cid, metrics)
if metrics:
wca_metrics = container.get_wca_metrics(app)
metric_list.extend(wca_metrics)
if self.mode_config == ContentionDetector.COLLECT_MODE:
app = self._cid_to_app(cid, tasks_labels)
if app:
self._record_metrics(timestamp, cid, app, metrics)
if self.mode_config == ContentionDetector.COLLECT_MODE:
self._record_utils(timestamp, lcutil)
elif self.mode_config == ContentionDetector.DETECT_MODE:
metric_list.extend(
self._get_headroom_metrics(assigned_cpus, lcutil, sysutil))
def detect(self, platform: Platform, tasks_measurements: TasksMeasurements,
tasks_resources: TasksResources, tasks_labels: TasksLabels):
log.debug('prm detect called...')
log.debug('task_labels=%r', tasks_labels)
self.counter += 1
if self.counter == self.agg_cnt:
self.counter = 0
self.agg = True
else:
self.agg = False
assigned_cpus = self._get_task_resources(tasks_resources, tasks_labels)
metric_list = []
metric_list.extend(self._get_threshold_metrics())
self._process_measurements(tasks_measurements, tasks_labels,
metric_list, platform.timestamp,
assigned_cpus)
anomaly_list = []
if self.agg:
if self.mode_config == ContentionDetector.DETECT_MODE:
for container in self.container_map.values():
app = self._cid_to_app(container.cid, tasks_labels)
if app:
anomalies = self._detect_one_task(container, app)
anomaly_list.extend(anomalies)
if anomaly_list:
log.debug('anomalies: %r', anomaly_list)
if metric_list:
log.debug('metrics: %r', metric_list)
return anomaly_list, metric_list
class ResourceAllocator(Allocator):
WL_META_FILE = 'workload.json'
def __init__(
self,
database: ModelDatabase,
action_delay: float,
agg_period: float = 20,
model_pull_cycle: float = 180,
metric_file: str = Analyzer.METRIC_FILE,
enable_control: bool = True,
exclusive_cat: bool = False
):
log.debug('action_delay: %i, agg_period: %i, exclusive: %s, model_pull_cycle: %i',
action_delay, agg_period, exclusive_cat, model_pull_cycle)
self.metric_file = metric_file
self.exclusive_cat = exclusive_cat
self.enable_control = enable_control
self.agg_cnt = int(agg_period) / int(action_delay) \
if int(agg_period) % int(action_delay) == 0 else 1
self.counter = 0
self.agg = False
self.container_map = dict()
self.bes = set()
self.lcs = set()
self.ucols = ['time', 'cid', 'name', Metric.UTIL]
self.mcols = ['time', 'cid', 'name', 'cpu_model', 'vcpu_count',
Metric.CYC, Metric.INST,
Metric.L3MISS, Metric.L3OCC, Metric.MB, Metric.CPI,
Metric.L3MPKI, Metric.NF, Metric.UTIL, Metric.MSPKI]
self.workload_meta = {}
self.analyzer = Analyzer()
if database:
self.database = database
self.model_pull_cycle = model_pull_cycle
self.threshs = {}
self.cycle = 0
if enable_control:
self.cpuc = CpuCycle(self.analyzer.get_lcutilmax(), 0.5, False)
self.l3c = LlcOccup(self.exclusive_cat)
self.mbc_enabled = True
self.mbc = MemoryBw()
cpuc_controller = NaiveController(self.cpuc, 15)
llc_controller = NaiveController(self.l3c, 4)
mb_controller = NaiveController(self.mbc, 4)
self.controllers = {ContendedResource.CPUS: cpuc_controller,
ContendedResource.LLC: llc_controller,
ContendedResource.MEMORY_BW: mb_controller}
def _init_data_file(self, data_file, cols):
headline = None
try:
with open(data_file, 'r') as dtf:
headline = dtf.readline()
except Exception:
log.debug('cannot open %r for reading - ignore', data_file)
if headline != ','.join(cols) + '\n':
with open(data_file, 'w') as dtf:
dtf.write(','.join(cols) + '\n')
def _detect_contenders(self, con: Container, resource: ContendedResource):
contenders = []
if resource == ContendedResource.UNKN:
return contenders
resource_delta_max = -np.Inf
contender_id = None
for cid, container in self.container_map.items():
delta = 0
if con.cid == container.cid:
continue
if resource == ContendedResource.LLC:
delta = container.get_llcoccupany_delta()
elif resource == ContendedResource.MEMORY_BW:
delta = container.get_latest_mbt()
elif resource == ContendedResource.TDP:
delta = container.get_freq_delta()
if delta > 0 and delta > resource_delta_max:
resource_delta_max = delta
contender_id = container.cid
if contender_id:
contenders.append(contender_id)
return contenders
def _append_anomaly(self, anomalies, res, cid, contenders, wca_metrics):
anomaly = ContentionAnomaly(
resource=res,
contended_task_id=cid,
contending_task_ids=contenders,
metrics=wca_metrics
)
anomalies.append(anomaly)
def _get_thresholds(self, app: str, thresh_type: ThreshType):
thresh = {}
vcpus = str(self.workload_meta[app]['cpus'])
if self.threshs and app in self.threshs and vcpus in self.threshs[app]:
thresh = self.threshs[app][vcpus][thresh_type.value]
return thresh
def _detect_one_task(self, con: Container, app: str):
anomalies = []
if not con.get_metrics():
return anomalies
cid = con.cid
thresh = self._get_thresholds(app, ThreshType.METRICS)
if thresh:
contends, wca_metrics = con.contention_detect(thresh)
log.debug('cid=%r contends=%r', cid, contends)
log.debug('cid=%r threshold metrics=%r', cid, wca_metrics)
for contend in contends:
contenders = self._detect_contenders(con, contend)
self._append_anomaly(anomalies, contend, cid, contenders,
wca_metrics)
thresh_tdp = self._get_thresholds(app, ThreshType.TDP)
if thresh_tdp:
tdp_contend, wca_metrics = con.tdp_contention_detect(thresh_tdp)
if tdp_contend:
contenders = self._detect_contenders(con, tdp_contend)
self._append_anomaly(anomalies, tdp_contend, cid, contenders,
wca_metrics)
return anomalies
def _remove_finished_tasks(self, cidset: set):
for cid in self.container_map.copy():
if cid not in cidset:
del self.container_map[cid]
def _cid_to_app(self, cid, tasks_labels: TasksLabels):
"""
Maps container id to a string key identifying statistical model instance.
"""
if 'application' in tasks_labels[cid]:
app = tasks_labels[cid]['application']
if 'application_version_name' in tasks_labels[cid] and\
tasks_labels[cid]['application_version_name']:
return app + '.' + tasks_labels[cid]['application_version_name']
else:
return app
else:
log.warn('no label "application" '
'passed to detect function by wca for container: {}'.format(cid))
return None
def _is_be_app(self, cid, tasks_labels: TasksLabels):
if 'type' in tasks_labels[cid] and tasks_labels[cid]['type'] == 'best_efforts':
return True
return False
def _get_headroom_metrics(self, assign_cpus, lcutil, sysutil):
util_max = self.analyzer.get_lcutilmax()
if util_max < lcutil:
self.analyzer.update_lcutilmax(lcutil)
if self.enable_control:
self.cpuc.update_max_sys_util(lcutil)
util_max = lcutil
capacity = assign_cpus * 100
return [WCAMetric(name=Metric.LCCAPACITY, value=capacity),
WCAMetric(name=Metric.LCMAX, value=util_max),
WCAMetric(name=Metric.SYSUTIL, value=sysutil)]
def _get_threshold_metrics(self):
"""Encode threshold objects as WCA metrics.
In contrast to *_threshold metrics from Container,
all utilization partitions are exposed for all workloads.
"""
metrics = []
# Only when debugging is enabled.
if log.getEffectiveLevel() == logging.DEBUG:
for cid, threshold in self.analyzer.threshold.items():
if cid == 'lcutilmax':
metrics.append(
WCAMetric(name='threshold_lcutilmax', value=threshold)
)
continue
if 'tdp' in threshold and 'bar' in threshold['tdp']:
metrics.extend([
WCAMetric(
name='threshold_tdp_bar',
value=threshold['tdp']['bar'],
labels=dict(cid=cid)),
WCAMetric(
name='threshold_tdp_util',
value=threshold['tdp']['util'],
labels=dict(cid=cid)),
])
if 'thresh' in threshold:
for d in threshold['thresh']:
metrics.extend([
WCAMetric(
name='threshold_cpi',
labels=dict(start=str(int(d['util_start'])),
end=str(int(d['util_end'])),
cid=cid),
value=d['cpi']),
WCAMetric(
name='threshold_mpki',
labels=dict(start=str(int(d['util_start'])),
end=str(int(d['util_end'])),
cid=cid),
value=(d['mpki'])),
WCAMetric(
name='threshold_mb',
labels=dict(start=str(int(d['util_start'])),
end=str(int(d['util_end'])),
cid=cid),
value=(d['mb'])),
])
return metrics
def _record_utils(self, time, utils):
row = [str(time), '', 'lcs']
for i in range(3, len(self.ucols)):
row.append(str(utils))
with open(Analyzer.UTIL_FILE, 'a') as utilf:
utilf.write(','.join(row) + '\n')
def _record_metrics(self, time, cid, name, cpu_model, vcpus, metrics):
row = [str(time), cid, name, cpu_model, str(vcpus)]
for i in range(5, len(self.mcols)):
row.append(str(metrics[self.mcols[i]]))
with open(self.metric_file, 'a') as metricf:
metricf.write(','.join(row) + '\n')
def _update_workload_meta(self):
with open(ResourceAllocator.WL_META_FILE, 'w') as wlf:
wlf.write(json.dumps(self.workload_meta))
def _get_task_resources(self, tasks_resources: TasksResources,
tasks_labels: TasksLabels):
assigned_cpus = 0
cidset = set()
for cid, resources in tasks_resources.items():
cidset.add(cid)
if not self._is_be_app(cid, tasks_labels):
assigned_cpus += resources['cpus']
if self.exclusive_cat:
self.lcs.add(cid)
else:
self.bes.add(cid)
app = self._cid_to_app(cid, tasks_labels)
if app:
self.workload_meta[app] = resources
self._update_workload_meta()
self._remove_finished_tasks(cidset)
return assigned_cpus
def _process_measurements(
self,
tasks_measurements: TasksMeasurements,
tasks_labels: TasksLabels,
metric_list: List[WCAMetric],
timestamp: float,
assigned_cpus: float,
cpu_model: str
):
sysutil = 0
lcutil = 0
beutil = 0
for cid, measurements in tasks_measurements.items():
app = self._cid_to_app(cid, tasks_labels)
if cid in self.container_map:
container = self.container_map[cid]
else:
container = Container(cid)
self.container_map[cid] = container
if self.enable_control:
if cid in self.bes:
self.cpuc.set_share(cid, 0.0)
self.cpuc.budgeting([cid], [])
self.l3c.budgeting([cid], [])
if self.mbc_enabled:
self.mbc.budgeting([cid], [])
else:
self.cpuc.set_share(cid, 1.0)
if self.exclusive_cat:
self.l3c.budgeting([], [cid])
container.update_measurement(timestamp, measurements, self.agg)
if cid not in self.bes:
lcutil += container.util
else:
beutil += container.util
sysutil += container.util
if self.agg:
metrics = container.get_metrics()
log.debug('cid=%r container metrics=%r', cid, metrics)
if metrics and app:
vcpus = self.workload_meta[app]['cpus']
wca_metrics = container.get_wca_metrics(app, vcpus)
metric_list.extend(wca_metrics)
# always try to init header column considering log rotate
self._init_data_file(self.metric_file, self.mcols)
self._record_metrics(timestamp, cid, app,
correct_key_characters(cpu_model),
vcpus, metrics)
metric_list.extend(self._get_headroom_metrics(assigned_cpus, lcutil, sysutil))
if self.enable_control and self.bes:
exceed, hold = self.cpuc.detect_margin_exceed(lcutil, beutil)
self.controllers[ContendedResource.CPUS].update(self.bes, [], exceed, hold)
def _allocate_resources(self, anomalies: List[ContentionAnomaly]):
contentions = {
ContendedResource.LLC: False,
ContendedResource.MEMORY_BW: False,
ContendedResource.UNKN: False
}
for anomaly in anomalies:
contentions[anomaly.resource] = True
for contention, flag in contentions.items():
if contention in self.controllers:
self.controllers[contention].update(self.bes, self.lcs, flag, False)
def allocate(
self,
platform: Platform,
tasks_measurements: TasksMeasurements,
tasks_resources: TasksResources,
tasks_labels: TasksLabels,
tasks_allocs: TasksAllocations):
log.debug('prm allocate called...')
log.debug('platform=%r', platform)
log.debug('tasks_resources=%r', tasks_resources)
log.debug('tasks_labels=%r', tasks_labels)
log.debug('current tasks_allocations=%r', tasks_allocs)
self.counter += 1
if self.counter == self.agg_cnt:
self.counter = 0
self.agg = True
else:
self.agg = False
self.bes.clear()
self.lcs.clear()
assigned_cpus = self._get_task_resources(tasks_resources, tasks_labels)
if platform.rdt_information is None:
log.error('ERROR: RDT is required for PRM plugin to work! Exiting.')
exit(1)
elif not platform.rdt_information.rdt_cache_monitoring_enabled or \
not platform.rdt_information.rdt_mb_monitoring_enabled:
log.error('ERROR: RDT cache monitoring and memory bandwitdth '
'monitoring is required for PRM plugin to work! Exiting.')
exit(1)
allocs: TasksAllocations = dict()
if self.enable_control:
self.cpuc.update_allocs(tasks_allocs, allocs, platform.cpus)
self.l3c.update_allocs(
tasks_allocs, allocs, platform.rdt_information.cbm_mask,
platform.sockets)
if platform.rdt_information.rdt_mb_control_enabled:
self.mbc_enabled = True
self.mbc.update_allocs(
tasks_allocs, allocs,
platform.rdt_information.mb_min_bandwidth,
platform.rdt_information.mb_bandwidth_gran, platform.sockets)
else:
self.mbc_enabled = False
self.controllers[ContendedResource.MEMORY_BW] = \
self.controllers[ContendedResource.CPUS]
metric_list = []
metric_list.extend(self._get_threshold_metrics())
self._process_measurements(tasks_measurements, tasks_labels, metric_list,
platform.timestamp, assigned_cpus, platform.cpu_model)
anomaly_list = []
if self.agg:
if self.database and self.cycle == 0:
try:
threshs = self.database.get(platform.cpu_model)
self.threshs = json.loads(threshs)
if self.threshs:
log.info('pulled model thresholds=%r', self.threshs)
else:
log.warn('No model is pulled from model database!')
except Exception:
log.exception('error in pulling model from database')
self.cycle += 1
if self.cycle == self.model_pull_cycle:
self.cycle = 0
for container in self.container_map.values():
app = self._cid_to_app(container.cid, tasks_labels)
if app and container.cid not in self.bes:
anomalies = self._detect_one_task(container, app)
anomaly_list.extend(anomalies)
if anomaly_list:
log.debug('anomalies: %r', anomaly_list)
if self.enable_control:
self._allocate_resources(anomaly_list)
if metric_list:
log.debug('metrics: %r', metric_list)
if allocs:
log.debug('allocs: %r', allocs)
return allocs, anomaly_list, metric_list