def test_random_scheduler(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.scheduler.at_random.num_machines = 100
simulation_config.scheduler.at_random.seed = 11
random.seed(simulation_config.scheduler.at_random.seed)
simulated_machine = random.randint(
0, simulation_config.scheduler.at_random.num_machines)
vmsample = [
{
"simulated_time": 300_000_000,
"simulated_machine": 1,
"sample": {
"time": 300_000_000,
"info": {
"unique_id": "1-2",
"machine_id": 3
},
"metrics": {
"avg_cpu_usage": 0.8,
},
"abstract_metrics": {
"usage": 1,
"limit": 1
},
},
},
]
def test_priority_filter(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.filter.priority_range.lower_bound = 3
simulation_config.filter.priority_range.upper_bound = 7
correct_output = [
{
"time": 20,
"info": {
"priority": 4,
"scheduling_class": 1,
"machine_id": 200,
"alloc_collection_id": 1,
},
},
{
"time": 30,
"info": {
"priority": 6,
"scheduling_class": 2,
"machine_id": 300,
"alloc_collection_id": 0,
},
},
]
with TestPipeline(runner=direct_runner.BundleBasedDirectRunner()) as p:
input_vmsamples = p | "Create priority test input" >> beam.Create(
self.multiple_vmsamples)
output = FilterVMSample(input_vmsamples, simulation_config)
assert_that(output, equal_to(correct_output))
def test_all_filters(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.filter.remove_non_top_level_vms = True
simulation_config.filter.priority_range.lower_bound = 1
simulation_config.filter.priority_range.upper_bound = 7
simulation_config.filter.scheduling_class_range.lower_bound = 0
simulation_config.filter.scheduling_class_range.upper_bound = 1
simulation_config.filter.start_time = 5
simulation_config.filter.end_time = 15
correct_output = [
{
"time": 10,
"info": {
"priority": 2,
"scheduling_class": 0,
"machine_id": 100,
"alloc_collection_id": 0,
},
},
]
with TestPipeline(runner=direct_runner.BundleBasedDirectRunner()) as p:
input_vmsamples = p | "Create all filter input" >> beam.Create(
self.multiple_vmsamples)
output = FilterVMSample(input_vmsamples, simulation_config)
assert_that(output, equal_to(correct_output))
def test_top_level_filter(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.filter.remove_non_top_level_vms = True
correct_output = [
{
"time": 10,
"info": {
"priority": 2,
"scheduling_class": 0,
"machine_id": 100,
"alloc_collection_id": 0,
},
},
{
"time": 30,
"info": {
"priority": 6,
"scheduling_class": 2,
"machine_id": 300,
"alloc_collection_id": 0,
},
},
]
with TestPipeline(runner=direct_runner.BundleBasedDirectRunner()) as p:
input_vmsamples = p | "Create top level filter input" >> beam.Create(
self.multiple_vmsamples)
output = FilterVMSample(input_vmsamples, simulation_config)
assert_that(output, equal_to(correct_output))
def setUp(self):
self.simulation_config = config_pb2.SimulationConfig()
self.simulation_config.reset_and_shift.reset_time_to_zero = True
self.simulation_config.reset_and_shift.random_shift.lower_bound = 0
self.simulation_config.reset_and_shift.random_shift.upper_bound = 600
self.simulation_config.reset_and_shift.seed = 11
random.seed(self.simulation_config.reset_and_shift.seed)
self.random_shift = _MinutesToMicroseconds(5 * (random.randrange(
self.simulation_config.reset_and_shift.random_shift.lower_bound,
self.simulation_config.reset_and_shift.random_shift.upper_bound,
) // 5))
def test_time_filter(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.filter.start_time = 15
simulation_config.filter.end_time = 25
correct_output = [
{
"time": 20,
"info": {
"priority": 4,
"scheduling_class": 1,
"machine_id": 200,
"alloc_collection_id": 1,
},
},
]
with TestPipeline(runner=direct_runner.BundleBasedDirectRunner()) as p:
input_vmsamples = p | "Create time test input" >> beam.Create(
self.multiple_vmsamples)
output = FilterVMSample(input_vmsamples, simulation_config)
assert_that(output, equal_to(correct_output))
def test_schedule_by_machine_id(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.scheduler.by_machine_id = True
vmsample = [
{
"simulated_time": 300_000_000,
"simulated_machine": 1,
"sample": {
"time": 300_000_000,
"info": {
"unique_id": "1-2",
"machine_id": 3
},
"metrics": {
"avg_cpu_usage": 0.8,
},
"abstract_metrics": {
"usage": 1,
"limit": 1
},
},
},
]
def test_setting_memory_metric(self):
simulation_config = config_pb2.SimulationConfig()
simulation_config.metric.max_memory_usage = True
correct_output = [{
"simulated_time": 1,
"simulated_machine": 1,
"sample": {
"time": 300000000,
"info": {
"unique_id": "1-2",
"collection_id": 1,
"instance_index": 2,
"priority": 6,
"scheduling_class": 3,
"machine_id": 3,
"alloc_collection_id": 0,
"alloc_instance_index": 5,
"collection_type": 0,
},
"metrics": {
"avg_cpu_usage": 0.8,
"avg_memory_usage": 8,
"max_cpu_usage": 0.1,
"max_memory_usage": 0.1,
"random_sample_cpu_usage": 0.11,
"random_sample_memory_usage": 12,
"assigned_memory": 13,
"sample_rate": 17,
"p0_cpu_usage": 0,
"p10_cpu_usage": 0.1,
"p20_cpu_usage": 0.2,
"p30_cpu_usage": 0.3,
"p40_cpu_usage": 0.4,
"p50_cpu_usage": 0.5,
"p60_cpu_usage": 0.6,
"p70_cpu_usage": 0.7,
"p80_cpu_usage": 0.8,
"p90_cpu_usage": 0.9,
"p91_cpu_usage": 0.91,
"p92_cpu_usage": 0.92,
"p93_cpu_usage": 0.93,
"p94_cpu_usage": 0.94,
"p95_cpu_usage": 0.95,
"p96_cpu_usage": 0.96,
"p97_cpu_usage": 0.97,
"p98_cpu_usage": 0.98,
"p99_cpu_usage": 0.99,
"memory_limit": 0.8,
"cpu_limit": 0.6,
},
"abstract_metrics": {
"usage": 0.1,
"limit": 0.8
},
},
}]
with TestPipeline(runner=direct_runner.BundleBasedDirectRunner()) as p:
input_vmsample = p | "Create test input" >> beam.Create(
self.vmsample)
output = SetAbstractMetrics(input_vmsample, simulation_config)
assert_that(output, equal_to(correct_output))
