def testClusterScalingWithPreemptableJobs(self):
"""
Test scaling simultaneously for a batch of preemptable and non-preemptable jobs.
"""
config = Config()
# Make defaults dummy values
config.defaultMemory = 1
config.defaultCores = 1
config.defaultDisk = 1
# Preemptable node parameters
config.nodeType = Shape(20, 10, 10, 10)
config.minNodes = 0
config.maxNodes = 10
# Preemptable node parameters
config.preemptableNodeType = Shape(20, 10, 10, 10)
config.minPreemptableNodes = 0
config.maxPreemptableNodes = 10
# Algorithm parameters
config.alphaPacking = 0.8
config.betaInertia = 1.2
config.scaleInterval = 3
self._testClusterScaling(config, numJobs=100, numPreemptableJobs=100)
def testClusterScaling(self):
"""
Test scaling for a batch of non-preemptable jobs and no preemptable jobs (makes debugging
easier).
"""
config = Config()
# Make defaults dummy values
config.defaultMemory = 1
config.defaultCores = 1
config.defaultDisk = 1
# No preemptable nodes/jobs
config.maxPreemptableNodes = 0 # No preemptable nodes
# Non-preemptable parameters
config.nodeType = Shape(20, 10, 10, 10)
config.minNodes = 0
config.maxNodes = 10
# Algorithm parameters
config.alphaPacking = 0.8
config.betaInertia = 1.2
config.scaleInterval = 3
self._testClusterScaling(config, numJobs=100, numPreemptableJobs=0)
def testClusterScalingWithPreemptableJobs(self):
"""
Test scaling simultaneously for a batch of preemptable and non-preemptable jobs.
"""
config = Config()
# Make defaults dummy values
config.defaultMemory = 1
config.defaultCores = 1
config.defaultDisk = 1
# Preemptable node parameters
config.nodeType = Shape(20, 10, 10, 10)
config.minNodes = 0
config.maxNodes = 10
# Preemptable node parameters
config.preemptableNodeType = Shape(20, 10, 10, 10)
config.minPreemptableNodes = 0
config.maxPreemptableNodes = 10
# Algorithm parameters
config.alphaPacking = 0.8
config.betaInertia = 1.2
config.scaleInterval = 3
self._testClusterScaling(config, numJobs=100, numPreemptableJobs=100)
def testClusterScaling(self):
"""
Test scaling for a batch of non-preemptable jobs and no preemptable jobs (makes debugging
easier).
"""
config = Config()
# Make defaults dummy values
config.defaultMemory = 1
config.defaultCores = 1
config.defaultDisk = 1
# No preemptable nodes/jobs
config.maxPreemptableNodes = 0 # No preemptable nodes
# Non-preemptable parameters
config.nodeType = Shape(20, 10, 10, 10)
config.minNodes = 0
config.maxNodes = 10
# Algorithm parameters
config.alphaPacking = 0.8
config.betaInertia = 1.2
config.scaleInterval = 3
self._testClusterScaling(config, numJobs=100, numPreemptableJobs=0)
def testClusterScalingWithPreemptableJobs(self):
"""
Test scaling simultaneously for a batch of preemptable and non-preemptable jobs.
"""
config = Config()
jobShape = Shape(20, 10, 10, 10, False)
preemptableJobShape = Shape(20, 10, 10, 10, True)
# Make defaults dummy values
config.defaultMemory = 1
config.defaultCores = 1
config.defaultDisk = 1
# non-preemptable node parameters
config.nodeTypes = [jobShape, preemptableJobShape]
config.minNodes = [0, 0]
config.maxNodes = [10, 10]
# Algorithm parameters
config.alphaPacking = 0.0
config.betaInertia = 1.2
config.scaleInterval = 3
self._testClusterScaling(config,
numJobs=100,
numPreemptableJobs=100,
jobShape=jobShape)
def testClusterScalingMultipleNodeTypes(self):
smallNode = Shape(20, 5, 10, 10, False)
mediumNode = Shape(20, 10, 10, 10, False)
largeNode = Shape(20, 20, 10, 10, False)
numJobs = 100
config = Config()
# Make defaults dummy values
config.defaultMemory = 1
config.defaultCores = 1
config.defaultDisk = 1
# No preemptable nodes/jobs
config.preemptableNodeTypes = []
config.minPreemptableNodes = []
config.maxPreemptableNodes = [] # No preemptable nodes
#Make sure the node types don't have to be ordered
config.nodeTypes = [largeNode, smallNode, mediumNode]
config.minNodes = [0, 0, 0]
config.maxNodes = [10, 10] # test expansion of this list
# Algorithm parameters
config.alphaPacking = 0.8
config.betaInertia = 1.2
config.scaleInterval = 3
mock = MockBatchSystemAndProvisioner(config, secondsPerJob=2.0)
clusterScaler = ClusterScaler(mock, mock, config)
clusterScaler.start()
mock.start()
try:
#Add small jobs
list(
map(lambda x: mock.addJob(jobShape=smallNode),
list(range(numJobs))))
list(
map(lambda x: mock.addJob(jobShape=mediumNode),
list(range(numJobs))))
#Add medium completed jobs
for i in range(1000):
iJ = JobNode(jobStoreID=1,
requirements=dict(memory=random.choice(
range(smallNode.memory, mediumNode.memory)),
cores=mediumNode.cores,
disk=largeNode.cores,
preemptable=False),
command=None,
jobName='testClusterScaling',
unitName='')
clusterScaler.addCompletedJob(iJ, random.choice(range(1, 10)))
while mock.getNumberOfJobsIssued() > 0 or mock.getNumberOfNodes(
) > 0:
logger.info("%i nodes currently provisioned" %
mock.getNumberOfNodes())
#Make sure there are no large nodes
self.assertEqual(mock.getNumberOfNodes(nodeType=largeNode), 0)
clusterScaler.check()
time.sleep(0.5)
finally:
clusterScaler.shutdown()
mock.shutDown()
#Make sure jobs ran on both the small and medium node types
self.assertTrue(mock.totalJobs > 0)
self.assertTrue(mock.maxWorkers[smallNode] > 0)
self.assertTrue(mock.maxWorkers[mediumNode] > 0)
self.assertEqual(mock.maxWorkers[largeNode], 0)