def setUp(self):
"""
Repeating above test cases but with delays to determine that delay
flags reach us.
Returns
-------
"""
self.env = simpy.Environment()
config = Config(INTEGRATION)
self.cluster = Cluster(self.env, config)
self.buffer = Buffer(self.env, self.cluster, config)
dm = DelayModel(0.9, "normal",
DelayModel.DelayDegree.HIGH)
self.planner = Planner(
self.env, PLANNING_ALGORITHM,
self.cluster, SHADOWPlanning('heft',delay_model=dm), delay_model=dm
)
self.scheduler = Scheduler(
self.env, self.buffer, self.cluster, DynamicAlgorithmFromPlan()
)
self.telescope = Telescope(
self.env, config, self.planner, self.scheduler
)
self.env.process(self.cluster.run())
self.env.process(self.buffer.run())
self.scheduler.start()
self.env.process(self.scheduler.run())
self.env.process(self.telescope.run())
def setUp(self) -> None:
"""
This test scenario is going to test the edge cases like
double-allocation that may happen.
For example - the use of curr_allocs should result in the
_process_current_schedule not double_allocation, so we will test this.
Returns
-------
"""
self.env = simpy.Environment()
config = Config(CONFIG)
sched_algorithm = DynamicAlgorithmFromPlan()
self.cluster = Cluster(env=self.env, config=config)
self.telescope = Telescope(self.env,
config,
planner=None,
scheduler=None)
self.buffer = Buffer(self.env, self.cluster, config)
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
sched_algorithm)
self.observation = self.telescope.observations[0]
self.machine = self.cluster.machines[0]
def setUp(self) -> None:
"""
Create a planner and a `simpy` environment in which to run dummy
simulations for the purpose of ensuring the planner works nicely
when selecting 'batch' as a static scheduling method.
Returns
-------
"""
self.env = simpy.Environment()
config = Config(CONFIG)
self.model = BatchPlanning('batch')
self.cluster = Cluster(self.env, config=config)
self.buffer = Buffer(env=self.env, cluster=self.cluster, config=config)
self.planner = Planner(
self.env,
PLAN_ALGORITHM,
self.cluster,
self.model,
)
self.telescope = Telescope(self.env,
config,
planner=None,
scheduler=None)
def setUp(self):
self.env = simpy.Environment()
config = Config(INTEGRATION)
self.cluster = Cluster(self.env, config)
self.buffer = Buffer(self.env, self.cluster, config)
self.planner = Planner(self.env, PLANNING_ALGORITHM, self.cluster,
SHADOWPlanning('heft'))
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
DynamicAlgorithmFromPlan())
self.telescope = Telescope(self.env, config, self.planner,
self.scheduler)
self.env.process(self.cluster.run())
self.env.process(self.buffer.run())
self.scheduler.start()
self.env.process(self.scheduler.run())
self.env.process(self.telescope.run())
def testTelescopeBasicConfig(self):
telescope = Telescope(env=self.env,
config=self.config,
planner=None,
scheduler=self.scheduler)
self.assertEqual(36, telescope.total_arrays)
# Pipelines are associated with individual observation
self.assertTrue('emu' in telescope.pipelines)
def setUp(self) -> None:
self.env = simpy.Environment()
config = Config(CONFIG)
self.cluster = Cluster(self.env, config)
self.buffer = Buffer(self.env, self.cluster, config)
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
DynamicAlgorithmFromPlan)
self.planner = Planner(self.env, PLANNING_ALGORITHM, self.cluster,
SHADOWPlanning('heft'))
# planner = None
self.telescope = Telescope(self.env, config, self.planner,
self.scheduler)
class TestSchedulerIntegration(unittest.TestCase):
def setUp(self):
self.env = simpy.Environment()
config = Config(INTEGRATION)
self.cluster = Cluster(self.env, config)
self.buffer = Buffer(self.env, self.cluster, config)
self.planner = Planner(self.env, PLANNING_ALGORITHM, self.cluster,
SHADOWPlanning('heft'))
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
DynamicAlgorithmFromPlan())
self.telescope = Telescope(self.env, config, self.planner,
self.scheduler)
self.env.process(self.cluster.run())
self.env.process(self.buffer.run())
self.scheduler.start()
self.env.process(self.scheduler.run())
self.env.process(self.telescope.run())
def test_FIFO_with_buffer(self):
"""
Demonstrate that the scheduler accurately schedules when we have
other Actors working in tandem.
Expectations:
- After 1 timestep in the simualtion, we have 5 resources
available of the 10 that we start with.
-
Returns
-------
"""
self.env.run(until=1)
self.assertEqual(10, len(self.cluster._resources['available']))
# This takes timestep, data in the HotBuffer should be 4
self.env.run(until=2)
self.assertEqual(5, len(self.cluster._resources['available']))
self.assertEqual(496, self.buffer.hot[0].current_capacity)
self.env.run(until=31)
self.assertEqual(5, len(self.cluster._tasks['finished']))
# self.assertEqual(500, self.buffer.hot[0].current_capacity)
self.assertEqual(210, self.buffer.cold[0].current_capacity)
self.env.run(until=32)
# Ensure the time
self.assertEqual(ScheduleStatus.ONTIME, self.scheduler.schedule_status)
# 30 timesteps until we finish everything + 81 timesteps to complete
# workflow plan.
self.env.run(until=124)
# As we have been processing the current observation, we are also
# ingestting the next one.
self.assertEqual(250, self.buffer.cold[0].current_capacity)
def setUp(self):
self.env = simpy.Environment()
sched_algorithm = DynamicAlgorithmFromPlan()
config = Config(LONG_CONFIG)
self.cluster = Cluster(self.env, config)
planning_model = SHADOWPlanning(algorithm=PLANNING_ALGORITHM)
self.planner = Planner(self.env, PLANNING_ALGORITHM, self.cluster,
planning_model)
self.buffer = Buffer(self.env, self.cluster, config)
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
sched_algorithm)
self.telescope = Telescope(self.env, config, self.planner,
self.scheduler)
def setUp(self) -> None:
"""
Setup a cluster and a simulation environment to test ingest pipelines
"""
self.env = simpy.Environment()
config = Config(CONFIG)
self.cluster = Cluster(env=self.env, config=config)
self.telescope = Telescope(self.env,
config,
planner=None,
scheduler=None)
self.observation = self.telescope.observations[0]
def setUp(self) -> None:
"""
Batch scheduling setup
"""
self.env = simpy.Environment()
config = Config(CONFIG)
self.cluster = Cluster(env=self.env, config=config)
self.telescope = Telescope(self.env,
config,
planner=None,
scheduler=None)
self.observation = self.telescope.observations[0]
def testIngest(self):
telescope = Telescope(env=self.env,
config=self.config,
planner=self.planner,
scheduler=self.scheduler)
self.assertEqual(0, telescope.telescope_use)
self.env.process(telescope.run())
self.env.process(self.cluster.run())
self.scheduler.start()
self.env.process(self.scheduler.run())
self.env.process(self.buffer.run())
self.env.run(until=2)
self.assertEqual(36, telescope.telescope_use)
self.assertEqual(5, len(self.cluster._resources['available']))
# After 1 timestep, data in the HotBuffer should be 2
self.assertEqual(496, self.buffer.hot[0].current_capacity)
self.env.run(until=11)
self.assertEqual(len([self.buffer.hot[0].observations["transfer"]]), 1)
self.assertEqual(462, self.buffer.hot[0].current_capacity)
self.assertEqual(248, self.buffer.cold[0].current_capacity)
self.env.run(until=12)
self.assertEqual(0, telescope.telescope_use)
self.assertEqual(10, len(self.cluster._resources['available']))
self.assertEqual(5, len(self.cluster._tasks['finished']))
def setUp(self) -> None:
"""
TODO
Set up tasks and cluster to attempt allocation of tasks to resources
"""
self.env = simpy.Environment()
config = Config(CONFIG)
self.cluster = Cluster(env=self.env, config=config)
self.telescope = Telescope(self.env,
config,
planner=None,
scheduler=None)
self.observation = self.telescope.observations[0]
self.machine = self.cluster.machines[0]
self.task = Task('test_0', 0, 2, self.machine, [])
def setUp(self):
self.algorithm = BatchProcessing
self.env = simpy.Environment()
config = Config(CONFIG)
self.cluster = Cluster(self.env, config=config)
self.buffer = Buffer(self.env, self.cluster, config)
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
DynamicAlgorithmFromPlan())
self.algorithm = BatchProcessing()
self.model = BatchPlanning('batch')
self.planner = Planner(
self.env,
'heft',
self.cluster,
self.model,
)
self.telescope = Telescope(self.env, config, self.planner,
self.scheduler)
class TestSchedulerDelayHelpers(unittest.TestCase):
def setUp(self):
self.env = simpy.Environment()
config = Config(INTEGRATION)
self.cluster = Cluster(self.env, config)
self.buffer = Buffer(self.env, self.cluster, config)
self.planner = Planner(self.env,
PLANNING_ALGORITHM,
self.cluster,
SHADOWPlanning('heft'),
delay_model=DelayModel(0.3, "normal"))
self.scheduler = Scheduler(self.env, self.buffer, self.cluster,
DynamicAlgorithmFromPlan())
self.telescope = Telescope(self.env, config, self.planner,
self.scheduler)
self.env.process(self.cluster.run())
self.env.process(self.buffer.run())
self.scheduler.start()
self.env.process(self.scheduler.run())
self.env.process(self.telescope.run())
def test_propogate_delay_returns_updated_workflow(self):
"""
class TestDelaysInActors(unittest.TestCase):
def setUp(self):
"""
Repeating above test cases but with delays to determine that delay
flags reach us.
Returns
-------
"""
self.env = simpy.Environment()
config = Config(INTEGRATION)
self.cluster = Cluster(self.env, config)
self.buffer = Buffer(self.env, self.cluster, config)
dm = DelayModel(0.9, "normal",
DelayModel.DelayDegree.HIGH)
self.planner = Planner(
self.env, PLANNING_ALGORITHM,
self.cluster, SHADOWPlanning('heft',delay_model=dm), delay_model=dm
)
self.scheduler = Scheduler(
self.env, self.buffer, self.cluster, DynamicAlgorithmFromPlan()
)
self.telescope = Telescope(
self.env, config, self.planner, self.scheduler
)
self.env.process(self.cluster.run())
self.env.process(self.buffer.run())
self.scheduler.start()
self.env.process(self.scheduler.run())
self.env.process(self.telescope.run())
def test_scheduler_delay_detection(self):
"""
Nothing should change until we reach the workflow plan, as we are
testing TaskDelays
Returns
-------
"""
self.env.run(until=1)
# Remember - env starts at 0, we don't start until 1.
self.assertEqual(10, len(self.cluster._resources['available']))
self.env.run(until=2)
# After 1 timestep, data in the HotBuffer should be 4
self.assertEqual(496, self.buffer.hot[0].current_capacity)
self.env.run(until=31)
self.assertEqual(5, len(self.cluster._tasks['finished']))
self.assertEqual(500, self.buffer.hot[0].current_capacity)
self.env.run(until=44)
# We know that the schedule has been delayed - however, we don't
# report this to the telescope until we know how long we are delayed
# (that is, until the task has completely finished its duration).
# In this instance. we know that the first task is going to be
# delayed, and so wait until it's completed execution to trigger a
# delay.
self.assertEqual(ScheduleStatus.ONTIME, self.scheduler.schedule_status)
self.env.run(until=45)
self.assertTrue(ScheduleStatus.DELAYED,self.scheduler.schedule_status)
self.env.run(until=124)
# Assert that we still have tasks running
# self.assertLess(
# 0, len(self.cluster.clusters['default']['tasks']['running'])
# )
self.assertNotEqual(250, self.buffer.cold[0].current_capacity)
def test_telescope_delay_detection(self):
"""
Returns
-------
"""
self.env.run(until=1)
# Remember - env starts at 0, we don't start until 1.
self.assertEqual(10, len(self.cluster._resources['available']))
self.env.run(until=2)
# After 1 timestep, data in the HotBuffer should be 4
self.assertEqual(496, self.buffer.hot[0].current_capacity)
self.env.run(until=31)
self.assertEqual(5, len(self.cluster._tasks['finished']))
self.assertEqual(500, self.buffer.hot[0].current_capacity)
self.env.run(until=32)
# Ensure the time
self.assertEqual(ScheduleStatus.ONTIME, self.scheduler.schedule_status)
self.env.run(until=100)
self.assertEqual(ScheduleStatus.DELAYED,self.scheduler.schedule_status)
self.assertTrue(self.telescope.delayed)
def test_telescope_delay_greedy_decision(self):
"""