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:
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]
if entry_point is None:
entry_point = ""
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())
def test_construction(self):
identifier = uuid.uuid4()
thread_count = 2
workload_type = STATIC
w = Workload(identifier, thread_count, workload_type)
self.assertEqual(identifier, w.get_id())
self.assertEqual(thread_count, w.get_thread_count())
self.assertEqual(workload_type, w.get_type())
def test_free_cpu(self):
for allocator_class in ALLOCATORS:
cpu = get_cpu()
allocator = allocator_class(cpu)
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT, len(cpu.get_empty_threads()))
w = Workload(uuid.uuid4(), 3, STATIC)
allocator.assign_threads(w)
self.assertEqual(
DEFAULT_TOTAL_THREAD_COUNT - w.get_thread_count(),
len(cpu.get_empty_threads()))
allocator.free_threads(w.get_id())
self.assertEqual(DEFAULT_TOTAL_THREAD_COUNT, len(cpu.get_empty_threads()))
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 _occupies_entire_cpu(workload: Workload, cpu: Cpu):
return len(cpu.get_threads()) == workload.get_thread_count()