def get_cpu_shares(workload: Workload) -> int:
if workload.is_opportunistic():
opportunistic_shares_scale = get_config_manager().get_int(
OPPORTUNISTIC_SHARES_SCALE_KEY, DEFAULT_OPPORTUNISTIC_SHARES_SCALE)
return workload.get_thread_count() * opportunistic_shares_scale
return workload.get_thread_count() * DEFAULT_SHARES_SCALE
def get_workload_response(workload: Workload,
cpu: Cpu) -> Optional[WorkloadAllocateResponse]:
thread_ids = get_threads(cpu, workload.get_id())
cpu_shares = get_cpu_shares(workload)
cpu_quota = get_cpu_quota(workload)
if len(thread_ids) < 1:
return None
memory_migrate = DEFAULT_TITUS_ISOLATE_MEMORY_MIGRATE
memory_spread_page = DEFAULT_TITUS_ISOLATE_MEMORY_SPREAD_PAGE
memory_spread_slab = DEFAULT_TITUS_ISOLATE_MEMORY_SPREAD_SLAB
config_manager = get_config_manager()
if config_manager is not None:
memory_migrate = config_manager.get_cached_bool(
TITUS_ISOLATE_MEMORY_MIGRATE, DEFAULT_TITUS_ISOLATE_MEMORY_MIGRATE)
memory_spread_page = config_manager.get_cached_bool(
TITUS_ISOLATE_MEMORY_SPREAD_PAGE,
DEFAULT_TITUS_ISOLATE_MEMORY_SPREAD_PAGE)
memory_spread_slab = config_manager.get_cached_bool(
TITUS_ISOLATE_MEMORY_SPREAD_SLAB,
DEFAULT_TITUS_ISOLATE_MEMORY_SPREAD_SLAB)
return WorkloadAllocateResponse(workload_id=workload.get_id(),
thread_ids=thread_ids,
cpu_shares=cpu_shares,
cpu_quota=cpu_quota,
memory_migrate=memory_migrate,
memory_spread_page=memory_spread_page,
memory_spread_slab=memory_spread_slab)
def assign_threads(workload: Workload) -> Cpu:
cpu = get_cpu()
threads = cpu.get_threads()
total_thread_count = workload.get_thread_count(
) + workload.get_opportunistic_thread_count()
for i in range(total_thread_count):
threads[i].claim(workload.get_id())
return cpu
def get_workload_response(workload: Workload,
cpu: Cpu) -> Optional[WorkloadAllocateResponse]:
thread_ids = get_threads(cpu, workload.get_id())
cpu_shares = get_cpu_shares(workload)
cpu_quota = get_cpu_quota(workload)
if len(thread_ids) < 1:
return None
return WorkloadAllocateResponse(workload.get_id(), thread_ids, cpu_shares,
cpu_quota)
def predict(self, workload: Workload,
cpu_usage_last_hour: np.array,
pred_env: PredEnvironment) -> float:
if workload.get_id() == 'static_a':
return workload.get_thread_count() * 0.8
elif workload.get_id() == 'static_b':
return workload.get_thread_count() * 0.01
elif workload.get_id() == 'burst_c':
return workload.get_thread_count() * 0.9
def predict(self, workload: Workload, cpu_usage_last_hour: np.array,
pred_env: PredEnvironment) -> float:
return workload.get_thread_count() * self.__constant_percent_busy / 100
def get_cpu_quota(workload: Workload) -> int:
if workload.is_burst():
return -1
return workload.get_thread_count() * DEFAULT_QUOTA_SCALE
def _occupies_entire_cpu(workload: Workload, cpu: Cpu):
return len(cpu.get_threads()) == workload.get_thread_count()
def get_duration(workload: Workload, percentile: float) -> Optional[float]:
for p in workload.get_duration_predictions():
if p.get_percentile() == percentile:
return p.get_duration()
return None
def predict(self, workload: Workload, cpu_usage_last_hour: np.array,
pred_env: PredEnvironment) -> float:
image = workload.get_image()
tokens = image.split('@')
valid_digest = False
if cpu_usage_last_hour is None:
cpu_usage_last_hour = np.full((60, ), np.nan, dtype=np.float32)
image_name = None
if len(tokens) == 2 and tokens[-1].startswith("sha256:"):
m = self._img_name_regex.search(tokens[0])
if m is not None:
valid_digest = True
image_name = m.groups(0)[0]
entry_point = workload.get_entrypoint()[:1000]
filter_key = "%s@%s" % (tokens[-1], entry_point)
if self.__use_whitelist and valid_digest and (
filter_key not in self.__model.filter):
# not in whitelist, predict without context features
q = Query2(
None, # image_name
None, # user
None, # app_name
workload.get_thread_count(),
None, # ram_requested
None, # disk_requested
None, # network_requested
None, # job_type
None, # region
None, # env
None, # hour of day
build_ts_features(cpu_usage_last_hour))
else:
q = Query2(image_name, workload.get_owner_email(),
workload.get_app_name(), workload.get_thread_count(),
workload.get_mem(), workload.get_disk(),
workload.get_network(),
workload.get_job_type().lower(), pred_env.region,
pred_env.nflx_env, pred_env.hour_of_day,
build_ts_features(cpu_usage_last_hour))
return min(self.__model.ml_model.predict_single(q),
workload.get_thread_count())