def measure(model, num_storages, output_dir):
env.init()
scheduler = "Fifo"
num_nodes = 1
cluster_config_path = env.get_cluter_config_path(
model, num_nodes, num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(
cluster_config_path, node_config_path, scheduler="Fifo",
model=model, num_nodes=num_nodes, num_storages=num_storages, parameters={})
upload = True
format = True
job_list = ["nocomputation"]
job_size_list = ["3GB"]
#job_list = ["wordcount", "nocomputation"]
#job_size_list = ["512MB", "1GB"]
map_size = 1024
submit_times = 10
job_timeline = jobfactory.create_all_pair_jobs(
job_list=job_list, job_size_list=job_size_list, map_size=map_size, submit_times=submit_times)
experiment = ExperimentRun(
job_timeline, output_dir, setting=setting, upload=upload,
format=format, sync=True)
experiment.init()
experiment.run()
def measure(model, scheduler, num_nodes, num_storages, output_dir):
cluster_config_path = env.get_cluter_config_path(model, num_nodes,
num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(cluster_config_path,
node_config_path,
scheduler="Fifo",
model=model,
num_nodes=num_nodes,
num_storages=num_storages,
parameters={})
upload = True
format = True
job_list = ["nocomputation"]
job_size_list = ["4GB"]
map_size = 1024
submit_times = 4
job_timeline = jobfactory.create_fixed_jobs(job_list=job_list,
job_size_list=job_size_list,
map_size=map_size,
submit_times=submit_times)
experiment = ExperimentRun(job_timeline,
output_dir,
setting=setting,
upload=upload,
format=format)
experiment.init()
experiment.run()
def measure(model, schedulers, num_nodes, num_storages, num_jobs, period,
output_dir):
parameters = {
'mapreduce.job.reduce.slowstart.completedmaps': '1.0',
'dfs.replication': '1',
}
cluster_config_path = env.get_cluter_config_path(model, num_nodes,
num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(cluster_config_path,
node_config_path,
scheduler="Fifo",
model=model,
num_nodes=num_nodes,
num_storages=num_storages,
parameters=parameters)
job_list = ["grep", "nocomputation", "histogrammovies", "histogramratings"]
job_size_list = ["1GB", "2GB", "4GB"]
job_timeline = jobfactory.create_jobs(job_list=job_list,
job_size_list=job_size_list,
num_jobs=num_jobs,
period=period)
experiment = SchedulerExperiment(setting, schedulers, job_timeline,
output_dir)
experiment.init()
experiment.run()
def measure(model,
schedulers,
num_nodes,
num_storages,
job_list,
num_jobs,
output_dir,
debug=False):
env.init(debug=debug)
from my.experiment.base import HadoopSetting
from my.experiment.base import SchedulerExperiment
from my.experiment import jobfactory
parameters = {
'mapreduce.job.reduce.slowstart.completedmaps': '0.8',
}
cluster_config_path = env.get_cluter_config_path(model, num_nodes,
num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(cluster_config_path,
node_config_path,
scheduler="Fifo",
model=model,
num_nodes=num_nodes,
num_storages=num_storages,
parameters=parameters)
#job_list = ["grep", "terasort", "wordcount", "nocomputation", "histogrammovies", "histogramratings", "custommap1"]
#job_list = ["grep"]
job_size_list = ["1GB"]
job_timeline = jobfactory.create_jobs(job_list=job_list,
job_size_list=job_size_list,
num_jobs=num_jobs,
period=10)
experiment = SchedulerExperiment(setting, schedulers, job_timeline,
output_dir)
experiment.init()
experiment.run()
def measure(model,
schedulers,
num_nodes,
num_storages,
submit_times,
submit_ratio,
output_dir,
debug=False):
if debug:
env.enable_debug()
parameters = {
'mapreduce.job.reduce.slowstart.completedmaps': '1.0',
'dfs.replication': '1',
}
cluster_config_path = env.get_cluter_config_path(model, num_nodes,
num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(cluster_config_path,
node_config_path,
scheduler="Fifo",
model=model,
num_nodes=num_nodes,
num_storages=num_storages,
parameters=parameters)
job_list = ["grep", "histogramratings"]
job_size = "4GB"
job_timeline = jobfactory.create_ab_jobs(job_list=job_list,
job_size=job_size,
submit_times=submit_times,
submit_ratio=submit_ratio)
experiment = SchedulerExperiment(setting, schedulers, job_timeline,
output_dir)
experiment.init()
experiment.run()
def measure(output_dir):
scheduler = "Fifo"
model = "decoupled"
num_nodes = 1
num_storages = 1
parameters = {}
upload = True
format = True
cluster_config_path = env.get_cluter_config_path(model, num_nodes,
num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(cluster_config_path,
node_config_path,
scheduler=scheduler,
model=model,
num_nodes=num_nodes,
num_storages=num_storages,
parameters=parameters)
job_list = [
"grep", "terasort", "wordcount", "nocomputation", "histogrammovies",
"histogramratings", "custommap1"
]
#job_list = ["nocomputation", "histogrammovies", "histogramratings", "custommap1"]
job_size_list = ["64MB"]
submit_times = 1
job_timeline = jobfactory.create_all_pair_jobs(job_list=job_list,
job_size_list=job_size_list,
submit_times=submit_times)
experiment = ExperimentRun(job_timeline,
output_dir,
setting=setting,
upload=upload,
format=format,
sync=True)
experiment.init()
experiment.run()
def measure(model, scheduler, num_nodes, num_storages, output_dir):
env.init()
cluster_config_path = env.get_cluter_config_path(model, num_nodes,
num_storages)
node_config_path = env.get_node_config_path()
setting = HadoopSetting(cluster_config_path,
node_config_path,
scheduler="Fifo",
model=model,
num_nodes=num_nodes,
num_storages=num_storages,
parameters={})
upload = True
format = True
job_list = [
"grep", "terasort", "wordcount", "nocomputation", "histogrammovies",
"histogramratings", "custommap1"
]
job_size_list = ["64MB", "128MB", "256MB", "512MB", "1GB", "2GB", "4GB"]
#job_list = ["wordcount", "nocomputation"]
#job_size_list = ["512MB", "1GB"]
map_size = 512
submit_times = 1
job_timeline = jobfactory.create_all_pair_jobs(job_list=job_list,
job_size_list=job_size_list,
map_size=map_size,
submit_times=submit_times)
experiment = ExperimentRun(job_timeline,
output_dir,
setting=setting,
upload=upload,
format=format,
sync=True)
experiment.init()
experiment.run()
