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Implement simple random spillback policy. (#1493)

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* spillback policy implementation: global + local scheduler

* modernize global scheduler policy state; factor out random number engine and generator

* Minimal version.

* Fix test.

* Make load balancing test less strenuous.
This commit is contained in:
Alexey Tumanov
2018-02-13 00:09:35 -08:00
committed by Robert Nishihara
parent f2b6a7b58d
commit 844a6afcdd
9 changed files with 162 additions and 32 deletions
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test/runtest.py
+4 -4
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@@ -1702,7 +1702,7 @@ class SchedulingAlgorithm(unittest.TestCase):
total_tasks,
num_local_schedulers,
minimum_count,
num_attempts=20):
num_attempts=100):
attempts = 0
while attempts < num_attempts:
locations = ray.get(
@@ -1728,11 +1728,11 @@ class SchedulingAlgorithm(unittest.TestCase):
@ray.remote
def f():
time.sleep(0.001)
time.sleep(0.01)
return ray.worker.global_worker.plasma_client.store_socket_name
self.attempt_to_load_balance(f, [], 100, num_local_schedulers, 25)
self.attempt_to_load_balance(f, [], 1000, num_local_schedulers, 250)
self.attempt_to_load_balance(f, [], 100, num_local_schedulers, 10)
self.attempt_to_load_balance(f, [], 1000, num_local_schedulers, 100)
def testLoadBalancingWithDependencies(self):
# This test ensures that tasks are being assigned to all local