def __remove_workload(self, workload_id):
log.info("Removing workload: {}".format(workload_id))
if workload_id not in self.__workloads:
log.error("Attempted to remove unknown workload: '{}'".format(
workload_id))
return
workload_map = self.get_workload_map_copy()
request = self.__get_threads_request(workload_id, workload_map, "free")
response = self.__cpu_allocator.free_threads(request)
workload_map.pop(workload_id)
self.__update_state(response, workload_map)
report_cpu_event(request, response)
def __init__(self):
self.__address = self.__get_address()
log.info("Set keystone address to: {}".format(self.__address))
self.__enabled = self.__address is not None
self.__q = Queue()
self.__reg = None
self.__succeeded_msg_count = 0
self.__retry_msg_count = 0
self.__failed_msg_count = 0
self.__processing_thread = Thread(target=self.__process_events)
self.__processing_thread.start()
def __init__(self, free_thread_provider):
config_manager = get_config_manager()
self.__url = config_manager.get_str(REMOTE_ALLOCATOR_URL,
"http://localhost:7501")
solver_max_runtime_secs = config_manager.get_float(
MAX_SOLVER_RUNTIME, DEFAULT_MAX_SOLVER_RUNTIME)
solver_max_connect_secs = config_manager.get_float(
MAX_SOLVER_CONNECT_SEC, DEFAULT_MAX_SOLVER_CONNECT_SEC)
self.__timeout = (solver_max_connect_secs, solver_max_runtime_secs)
self.__headers = {'Content-Type': "application/json"}
self.__reg = None
log.info("remote allocator max_connect_secs: %d, max_runtime_secs: %d",
solver_max_connect_secs, solver_max_runtime_secs)
def free_threads():
try:
body = request.get_json()
log.info("Processing free threads request: {}".format(body))
threads_request = get_threads_request(body)
response = get_free_cpu_allocator().free_threads(threads_request)
global free_threads_success_count
free_threads_success_count += 1
return jsonify(response.to_dict())
except:
log.exception("Failed to free threads")
global free_threads_failure_count
free_threads_failure_count += 1
return "Failed to free threads", 500
def test_assign_one_workload_empty_cpu(self):
cpu = get_cpu()
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT,
len(cpu.get_empty_threads()))
w = get_test_workload(uuid.uuid4(), 1, STATIC)
request = get_no_usage_threads_request(cpu, [w])
cpu = noop_reset_allocator.assign_threads(request).get_cpu()
log.info(cpu)
self.assertEqual(0, len(cpu.get_empty_threads()))
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT,
len(cpu.get_claimed_threads()))
for t in cpu.get_threads():
self.assertEqual(1, len(t.get_workload_ids()))
self.assertEqual(w.get_id(), t.get_workload_ids()[0])
def __watch(self):
while True:
try:
instance_id = get_config_manager().get_str("EC2_INSTANCE_ID")
field_selector = "spec.nodeName={}".format(instance_id)
log.info("Watching pods with field selector: %s",
field_selector)
v1 = client.CoreV1Api()
w = watch.Watch()
for event in w.stream(v1.list_pod_for_all_namespaces,
field_selector=field_selector):
self.__handle_event(event)
except:
log.exception("pod watch thread failed")
def rebalance():
try:
body = request.get_json()
log.info("Processing rebalance threads request: {}".format(body))
rebalance_request = get_rebalance_request(body)
response = get_rebalance_cpu_allocator().rebalance(rebalance_request)
global rebalance_success_count
rebalance_success_count += 1
return jsonify(response.to_dict())
except:
log.exception("Failed to rebalance")
global rebalance_failure_count
rebalance_failure_count += 1
return "Failed to rebalance", 500
def test_assign_more_than_available_threads_with_one_workload(self):
for allocator in OVER_ALLOCATORS:
cpu = get_cpu()
w_jumbo = get_test_workload("jumbo",
DEFAULT_TOTAL_THREAD_COUNT * 1.5,
STATIC)
request = AllocateThreadsRequest(cpu, w_jumbo.get_id(),
{w_jumbo.get_id(): w_jumbo}, {},
DEFAULT_TEST_REQUEST_METADATA)
cpu = allocator.assign_threads(request).get_cpu()
log.info(cpu)
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT,
len(cpu.get_claimed_threads()))
self.assertEqual([w_jumbo.get_id()],
list(cpu.get_workload_ids_to_thread_ids().keys()))
def __is_long_enough(self, workload) -> bool:
min_duration_sec = 60 * self.__config_manager.get_int(
OVERSUBSCRIBE_WINDOW_SIZE_MINUTES_KEY,
DEFAULT_OVERSUBSCRIBE_WINDOW_SIZE_MINUTES)
workload_duration_sec = self.__get_workload_duration(
workload, min_duration_sec)
if workload_duration_sec < min_duration_sec:
log.info(
"Workload: {} is too short. workload_duration_sec: {} < min_duration_sec: {}"
.format(workload.get_id(), workload_duration_sec,
min_duration_sec))
return False
log.info(
"Workload: {} is long enough. workload_duration_sec: {} >= min_duration_sec: {}"
.format(workload.get_id(), workload_duration_sec,
min_duration_sec))
return True
def __init__(self,
relative_start_sec: int,
interval_sec: int,
sample_interval_sec: int = DEFAULT_SAMPLE_FREQUENCY_SEC,
query_timeout_sec: int = DEFAULT_METRICS_QUERY_TIMEOUT_SEC):
self.__relative_start_sec = relative_start_sec
self.__interval_sec = interval_sec
self.__query_timeout_sec = query_timeout_sec
self.__interval_count = int(relative_start_sec / interval_sec)
self.__usages = None
self.__lock = Lock()
self.__snapshot_usage_raw()
log.info("Scheduling pcp metrics collecting every {} seconds".format(
sample_interval_sec))
schedule.every(sample_interval_sec).seconds.do(
self.__snapshot_usage_raw)
def __init__(
self,
config_manager: ConfigManager,
exit_handler: ExitHandler,
properties: List[str],
detection_interval: int = PROPERTY_CHANGE_DETECTION_INTERVAL_SEC):
self.__config_manager = config_manager
self.__exit_handler = exit_handler
self.__properties = properties
log.info("Starting watching for changes to properties: {}".format(
properties))
for p in properties:
v = config_manager.get_cached_str(p)
log.info("{}: {}".format(p, v))
schedule.every(detection_interval).seconds.do(self.detect_changes)
def rebalance(self, request: AllocateRequest) -> AllocateResponse:
self.__call_meta = {}
cpu = request.get_cpu()
cpu_usage = request.get_cpu_usage()
workloads = request.get_workloads()
self.__cnt_rebalance_calls += 1
if len(workloads) == 0:
log.warning("Ignoring rebalance of empty CPU.")
self.__call_meta['rebalance_empty'] = 1
return AllocateResponse(cpu, self.get_name(), self.__call_meta)
log.info("Rebalancing with predictions...")
curr_ids_per_workload = cpu.get_workload_ids_to_thread_ids()
return AllocateResponse(
self.__compute_allocation(cpu, None, workloads, curr_ids_per_workload, cpu_usage, None),
self.get_name(),
self.__call_meta)
def rebalance():
try:
request_ip = request.headers.get(FORWARDED_FOR_HEADER)
log.info("Processing rebalance threads request (from, proxy): {}".format(request_ip))
body = request.get_json()
rebalance_request = get_rebalance_request(body)
response = get_rebalance_cpu_allocator().rebalance(rebalance_request)
global rebalance_success_count
rebalance_success_count += 1
log.info("Processed rebalance threads request (from, proxy): {}".format(request_ip))
return jsonify(response.to_dict())
except:
log.exception("Failed to rebalance")
global rebalance_failure_count
rebalance_failure_count += 1
return "Failed to rebalance", 500
def get_current_workloads(docker_client):
workloads = []
for container in docker_client.containers.list():
workload_id = container.name
if __has_required_labels(container):
try:
labels = container.labels
cpu = int(__get_value(labels, CPU_LABEL_KEY, -1))
mem = int(__get_value(labels, MEM_LABEL_KEY, -1))
disk = int(__get_value(labels, DISK_LABEL_KEY, -1))
network = int(__get_value(labels, DISK_LABEL_KEY, -1))
app_name = __get_value(labels, APP_NAME_LABEL_KEY)
owner_email = __get_value(labels, OWNER_EMAIL_LABEL_KEY)
command = __get_value(labels, COMMAND_LABEL_KEY)
entrypoint = __get_value(labels, ENTRYPOINT_LABEL_KEY)
job_type = __get_value(labels, JOB_TYPE_LABEL_KEY)
workload_type = __get_value(labels, WORKLOAD_TYPE_LABEL_KEY)
image = __get_image(container)
workloads.append(
Workload(identifier=workload_id,
thread_count=cpu,
mem=mem,
disk=disk,
network=network,
app_name=app_name,
owner_email=owner_email,
image=image,
command=command,
entrypoint=entrypoint,
job_type=job_type,
workload_type=workload_type))
log.info("Found running workload: '{}'".format(workload_id))
except:
log.exception(
"Failed to parse labels for container: '{}'".format(
container.name))
else:
log.warning(
"Found running workload: '{}' without expected labels'")
return workloads
def get_current_workloads(docker_client):
workloads = []
for container in docker_client.containers.list():
workload_id = container.name
if __has_required_labels(container):
try:
cpu = int(container.labels[CPU_LABEL_KEY])
workload_type = container.labels[WORKLOAD_TYPE_LABEL_KEY]
workloads.append(Workload(workload_id, cpu, workload_type))
log.info("Found running workload: '{}'".format(workload_id))
except:
log.exception(
"Failed to parse labels for container: '{}'".format(
container.name))
else:
log.warning(
"Found running workload: '{}' without expected label: '{}'".
format(workload_id, CPU_LABEL_KEY))
return workloads
def __get_simple_cpu_predictions(self) -> Dict[str, float]:
cpu_predictor = self.__cpu_usage_predictor_manager.get_cpu_predictor()
if cpu_predictor is None:
log.error("Failed to get cpu predictor")
return {}
workloads = self.workload_manager.get_workloads()
resource_usage = GlobalResourceUsage(
self.__workload_monitor_manager.get_pcp_usage())
log.info("Getting simple cpu predictions...")
cpu_predictions = cpu_predictor.get_cpu_predictions(
workloads, resource_usage)
if cpu_predictions is None:
log.error("Failed to get cpu predictions")
return {}
else:
log.info("Got simple cpu predictions: %s",
json.dumps(cpu_predictions))
return cpu_predictions
def test_assign_one_thread_empty_cpu(self):
"""
Workload 0: 1 thread --> (p:0 c:0 t:0)
"""
for allocator in ALLOCATORS:
cpu = get_cpu()
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT,
len(cpu.get_empty_threads()))
w = get_test_workload(uuid.uuid4(), 1, STATIC)
request = get_no_usage_threads_request(cpu, [w])
cpu = allocator.assign_threads(request).get_cpu()
log.info(cpu)
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT - 1,
len(cpu.get_empty_threads()))
self.assertEqual(1, len(cpu.get_claimed_threads()))
self.assertEqual(
w.get_id(),
cpu.get_claimed_threads()[0].get_workload_ids()[0])
def __init__(self, cpu: Cpu, cgroup_manager: CgroupManager, cpu_allocator: CpuAllocator):
self.__reg = None
self.__lock = Lock()
self.__instance_id = get_config_manager().get_str(EC2_INSTANCE_ID)
self.__cpu_allocator = cpu_allocator
self.__error_count = 0
self.__added_count = 0
self.__removed_count = 0
self.__rebalanced_count = 0
self.__added_to_full_cpu_count = 0
self.__allocator_call_duration_sum_secs = 0
self.__cpu = cpu
self.__cgroup_manager = cgroup_manager
self.__wmm = get_workload_monitor_manager()
self.__workloads = {}
log.info("Created workload manager")
def get_workload_from_kubernetes(identifier) -> Optional[KubernetesWorkload]:
if not managers_are_initialized():
log.error(
"Cannot get workload from kubernetes because managers aren't initialized"
)
return None
retry_count = get_config_manager().get_int(
GET_WORKLOAD_RETRY_COUNT, DEFAULT_GET_WORKLOAD_RETRY_COUNT)
retry_interval = get_config_manager().get_float(
GET_WORKLOAD_RETRY_INTERVAL_SEC,
DEFAULT_GET_WORKLOAD_RETRY_INTERVAL_SEC)
pod_manager = get_pod_manager()
for i in range(retry_count):
log.info("Getting pod from kubernetes: %s", identifier)
pod = pod_manager.get_pod(identifier)
if pod is not None:
log.info("Got pod from kubernetes: %s", identifier)
return KubernetesWorkload(pod)
log.info("Retrying getting pod from kubernetes in %s seconds",
retry_interval)
time.sleep(retry_interval)
log.error("Failed to get pod from kubernetes: %s", identifier)
return None
def test_external_cpu_manipulation(self):
cpu = get_cpu()
violations = get_shared_core_violations(cpu)
log.info("shared core violations: {}".format(violations))
self.assertEqual(0, len(violations))
# Claim 1 thread on every core
dummy_workload_id = uuid.uuid4()
for p in cpu.get_packages():
for c in p.get_cores():
c.get_threads()[0].claim(dummy_workload_id)
violations = get_shared_core_violations(cpu)
log.info("shared core violations: {}".format(violations))
self.assertEqual(0, len(violations))
# Assign another workload which will force core sharing
allocator = GreedyCpuAllocator()
w = get_test_workload(uuid.uuid4(), 2, STATIC)
workloads = {w.get_id(): w}
request = AllocateThreadsRequest(cpu, w.get_id(), workloads, {},
DEFAULT_TEST_REQUEST_METADATA)
cpu = allocator.assign_threads(request).get_cpu()
violations = get_shared_core_violations(cpu)
log.info("shared core violations: {}".format(violations))
self.assertEqual(2, len(violations))
def __apply_isolation(self, response: AllocateResponse):
last_w_responses = self.__get_workload_allocation_dict(self.__last_response)
for w_alloc in response.get_workload_allocations():
last_w_alloc = last_w_responses.get(w_alloc.get_workload_id(), None)
if w_alloc == last_w_alloc:
log.info("Skipping update of workload: {}".format(w_alloc.get_workload_id()))
continue
workload_id = w_alloc.get_workload_id()
thread_ids = w_alloc.get_thread_ids()
quota = w_alloc.get_cpu_quota()
shares = w_alloc.get_cpu_shares()
log.info("updating workload: '{}' cpuset: '{}', quota: '{}', shares: '{}'".format(
workload_id, thread_ids, quota, shares))
# This ordering is important for reporting whether a workload is isolated.
# We must always set the "cpuset" first.
self.__cgroup_manager.set_cpuset(workload_id, thread_ids)
self.__cgroup_manager.set_quota(workload_id, quota)
self.__cgroup_manager.set_shares(workload_id, shares)
def __apply_isolation(self, response: AllocateResponse):
last_w_responses = self.__get_workload_allocation_dict(
self.__last_response)
for w_alloc in response.get_workload_allocations():
last_w_alloc = last_w_responses.get(w_alloc.get_workload_id(),
None)
if w_alloc == last_w_alloc:
log.info("Skipping update of workload: {}".format(
w_alloc.get_workload_id()))
continue
workload_id = w_alloc.get_workload_id()
thread_ids = w_alloc.get_thread_ids()
quota = w_alloc.get_cpu_quota()
shares = w_alloc.get_cpu_shares()
memory_migrate = w_alloc.get_memory_migrate()
memory_spread_page = w_alloc.get_memory_spread_page()
memory_spread_slab = w_alloc.get_memory_spread_slab()
log.info(f'updating workload: {workload_id} '
f'cpuset: {thread_ids}, '
f'quota: {quota}, '
f'shares: {shares}, '
f'memory_migrate: {memory_migrate}, '
f'memory_spread_page: {memory_spread_page}, '
f'memory_spread_slab: {memory_spread_slab}')
# This ordering is important for reporting whether a workload is isolated.
# We must always set the "cpuset" first.
self.__cgroup_manager.set_cpuset(workload_id, thread_ids)
self.__cgroup_manager.set_quota(workload_id, quota)
self.__cgroup_manager.set_shares(workload_id, shares)
self.__cgroup_manager.set_memory_migrate(workload_id,
memory_migrate)
self.__cgroup_manager.set_memory_spread_page(
workload_id, memory_spread_page)
self.__cgroup_manager.set_memory_spread_slab(
workload_id, memory_spread_slab)
def __predict_usage(self, workloads, cpu_usage):
res = {}
cpu_usage_predictor = self.__get_cpu_usage_predictor()
cm = self.__config_manager
pred_env = PredEnvironment(cm.get_region(), cm.get_environment(), dt.utcnow().hour)
start_time = time.time()
for w in workloads.values(): # TODO: batch the call
pred = cpu_usage_predictor.predict(w, cpu_usage.get(w.get_id(), None), pred_env)
res[w.get_id()] = pred
stop_time = time.time()
self.__call_meta['pred_cpu_usage_dur_secs'] = stop_time - start_time
try:
self.__call_meta['pred_cpu_usage_model_id'] = cpu_usage_predictor.get_model().meta_data['model_training_titus_task_id']
except:
self.__call_meta['pred_cpu_usage_model_id'] = 'unknown'
log.info("Usage prediction per workload: " + str(res))
if len(res) > 0:
self.__call_meta['pred_cpu_usage'] = dict(res)
return res
def test_crash_ip_allocator_metrics(self):
cpu = get_cpu(2, 16, 2)
test_context = TestContext(cpu=cpu)
# now override the cpu seen by the allocator to crash it
test_context.get_workload_manager().get_allocator().set_cpu(
get_cpu(2, 2, 2))
events = [get_container_create_event(10, name="foo", id="bar")]
event_count = len(events)
event_manager = EventManager(MockEventProvider(events),
test_context.get_event_handlers(),
get_mock_file_manager(), 5.0)
wait_until(lambda: event_count == event_manager.get_processed_count())
log.info("Event manager has processed {} events.".format(
event_manager.get_processed_count()))
workload_manager = test_context.get_workload_manager()
registry = Registry()
reporter = InternalMetricsReporter(workload_manager, event_manager)
reporter.set_registry(registry)
reporter.report_metrics({})
wait_until(lambda: self.__gauge_value_equals(registry, RUNNING, 1))
wait_until(lambda: self.__gauge_value_equals(registry, ADDED_KEY, 1))
wait_until(lambda: self.__gauge_value_equals(registry, REMOVED_KEY, 0))
wait_until(
lambda: self.__gauge_value_equals(registry, SUCCEEDED_KEY, 1))
wait_until(lambda: self.__gauge_value_equals(registry, FAILED_KEY, 0))
wait_until(
lambda: self.__gauge_value_equals(registry, WORKLOAD_COUNT_KEY, 1))
wait_until(lambda: self.__gauge_value_equals(
registry, FALLBACK_ALLOCATOR_COUNT, 1))
event_manager.stop_processing_events()
def __init__(self, exit_handler: ExitHandler):
self.__exit_handler = exit_handler
self.__config_manager = get_config_manager()
self.__node_name = self.__config_manager.get_str(EC2_INSTANCE_ID)
kubeconfig = self.get_kubeconfig_path()
self.__core_api = kubernetes.client.CoreV1Api(
kubernetes.config.new_client_from_config(config_file=kubeconfig))
self.__custom_api = kubernetes.client.CustomObjectsApi(
kubernetes.config.new_client_from_config(config_file=kubeconfig))
self.__lock = Lock()
self.__opportunistic_resources = {}
oversubscribe_frequency = self.__config_manager.get_float(
OVERSUBSCRIBE_FREQUENCY_KEY, DEFAULT_OVERSUBSCRIBE_FREQUENCY)
if oversubscribe_frequency > 0:
watch_thread = Thread(target=self.__watch)
watch_thread.start()
else:
log.info(
"Skipping opportunistic resource watch, as opportunistic publishing is not configured."
)
def __init__(self,
config_manager,
exit_handler,
properties,
detection_interval=PROPERTY_CHANGE_DETECTION_INTERVAL_SEC):
self.__config_manager = config_manager
self.__exit_handler = exit_handler
self.__properties = properties
self.__original_properties = {}
for p in properties:
self.__original_properties[p] = config_manager.get_str(p)
self.__original_primary_allocator_name =\
get_fallback_allocator(config_manager).get_primary_allocator().__class__.__name__
log.info("Starting watching for changes to properties: {}".format(
properties))
for k, v in self.__original_properties.items():
log.info("{}: {}".format(k, v))
schedule.every(detection_interval).seconds.do(self.detect_changes)
def __init__(self, cpu, cgroup_manager,
allocator_class=IntegerProgramCpuAllocator,
fallback_allocator_class=GreedyCpuAllocator):
self.__lock = Lock()
self.__error_count = 0
self.__added_count = 0
self.__removed_count = 0
self.__allocator_call_duration_sum_secs = 0
self.__fallback_allocator_calls_count = 0
self.__time_bound_ip_allocator_solution_count = 0
self.__cpu = cpu
self.__cgroup_manager = cgroup_manager
self.__workloads = {}
self.__cpu_allocator = allocator_class(cpu)
self.__is_ip_allocator_used = False
self.__fallback_cpu_allocator = None
if isinstance(self.__cpu_allocator, IntegerProgramCpuAllocator):
self.__is_ip_allocator_used = True
if fallback_allocator_class is not None:
self.__fallback_cpu_allocator = fallback_allocator_class(cpu)
log.info("Created workload manager with allocator: '{}'".format(self.__cpu_allocator.__class__.__name__))
def __init__(self, cpu: Cpu, cgroup_manager: CgroupManager, cpu_allocator: CpuAllocator):
self.__reg = None
self.__tags = None
self.__lock = Lock()
self.__instance_id = get_config_manager().get_str(EC2_INSTANCE_ID)
self.__cpu_allocator = cpu_allocator
self.__error_count = 0
self.__added_count = 0
self.__removed_count = 0
self.__rebalanced_count = 0
self.__workload_processing_duration_sec = 0
self.__update_state_duration_sec = 0
self.__cpu = cpu
self.__cgroup_manager = cgroup_manager
self.__wmm = get_workload_monitor_manager()
self.__workloads = {}
self.__last_response = None
log.info("Created workload manager")
def get_predictions(
self, pods: List[V1Pod], resource_usage: GlobalResourceUsage
) -> Optional[ResourceUsagePredictions]:
config_manager = get_config_manager()
if config_manager is None:
log.warning("Config manager not yet set.")
return None
running_pods = []
for p in pods:
if self.is_running(p):
running_pods.append(p)
else:
log.info("Pod is not yet running: %s", p.metadata.name)
client_crt = get_client_cert_path(config_manager)
client_key = get_client_key_path(config_manager)
if client_crt is None or client_key is None:
log.error("Failed to generate credential paths")
return None
url = get_url(config_manager)
if url is None:
log.error("Unable to generate prediction service url")
return None
body = self.__get_body(running_pods, resource_usage)
if body is None:
log.error("Unable to generate a prediction request body")
return None
predictions = get_predictions(client_crt, client_key, url, body)
if predictions is None:
log.error("Failed to get predictions")
return None
return ResourceUsagePredictions(predictions)
def __watch(self):
label_selector = "{}={}".format(
OPPORTUNISTIC_RESOURCE_NODE_NAME_LABEL_KEY, self.__node_name)
log.info("Starting opportunistic resource watch...")
stream = None
try:
stream = watch.Watch().stream(
self.__custom_api.list_cluster_custom_object,
group="titus.netflix.com",
version="v1",
plural="opportunistic-resources",
label_selector=label_selector)
for event in stream:
log.info("Event: %s", event)
if self.__is_expired_error(event):
raise Exception("Opportunistic resource expired")
event_type = event['type']
if event_type not in HANDLED_EVENTS:
log.warning("Ignoring unhandled event: %s", event)
continue
event_metadata_name = event['object']['metadata']['name']
with self.__lock:
if event_type == ADDED:
self.__opportunistic_resources[
event_metadata_name] = event
elif event_type == DELETED:
self.__opportunistic_resources.pop(
event_metadata_name, None)
except Exception:
if stream is not None:
stream.close()
log.exception("Watch of opportunistic resources failed")
self.__exit_handler.exit(OPPORTUNISTIC_WATCH_FAILURE)
