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Adding Alibaba configs from erdos3.
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# Output configs.
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--log_dir=experiments/alibaba_easy_medium_hard_20_slots/dagsched/arrival_rate_0.009_0.006/
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--log_file_name=alibaba_trace_replay_dagsched.log
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--csv_file_name=alibaba_trace_replay_dagsched.csv
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--log_level=debug
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# Workload configs.
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--execution_mode=replay
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--replay_trace=alibaba
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--workload_profile_paths=traces/alibaba-cluster-trace-v2018/easy_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/medium_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/hard_dag_sukrit_10k.pkl
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--workload_profile_path_labels=easy,medium,hard
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--override_release_policies=poisson,poisson,poisson
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--override_num_invocations=0,75,25
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--override_poisson_arrival_rates=0.0075,0.009,0.006
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--randomize_start_time_max=50
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--min_deadline=5
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--max_deadline=500
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--min_deadline_variances=25,50,10
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--max_deadline_variances=50,100,25
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# Worker configs.
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--worker_profile_path=./profiles/workers/alibaba_cluster.yaml
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# Loader configs.
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--alibaba_loader_task_cpu_divisor=10
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--alibaba_loader_min_critical_path_runtimes=200,500,600
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--alibaba_loader_max_critical_path_runtimes=500,1000,1000
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# Scheduler configs.
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--scheduler=TetriSched
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--release_taskgraphs
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--scheduler_runtime=0
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--enforce_deadlines
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--scheduler_time_discretization=1
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--scheduler_enable_optimization_pass
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--random_seed=420665456
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--retract_schedules
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--scheduler_time_limit=180
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# Output configs.
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--log_dir=experiments/alibaba_easy_medium_hard_20_slots/edf/arrival_rate_0.009_0.006/
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--log_file_name=alibaba_trace_replay_edf.log
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--csv_file_name=alibaba_trace_replay_edf.csv
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--log_level=debug
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# Workload configs.
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--execution_mode=replay
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--replay_trace=alibaba
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--workload_profile_paths=traces/alibaba-cluster-trace-v2018/easy_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/medium_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/hard_dag_sukrit_10k.pkl
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--workload_profile_path_labels=easy,medium,hard
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--override_release_policies=poisson,poisson,poisson
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--override_num_invocations=0,75,25
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--override_poisson_arrival_rates=0.0075,0.009,0.006
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--randomize_start_time_max=50
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--min_deadline=5
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--max_deadline=500
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--min_deadline_variances=25,50,10
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--max_deadline_variances=50,100,25
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# Worker configs.
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--worker_profile_path=./profiles/workers/alibaba_cluster.yaml
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# Loader configs.
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--alibaba_loader_task_cpu_divisor=10
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--alibaba_loader_min_critical_path_runtimes=200,500,600
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--alibaba_loader_max_critical_path_runtimes=500,1000,1000
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# Scheduler configs.
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--scheduler=EDF
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--scheduler_runtime=0
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--enforce_deadlines
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--random_seed=420665456
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# Output configs.
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--log_dir=experiments/alibaba_easy_medium_hard_20_slots/min_jct/arrival_rate_0.009_0.006/
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--log_file_name=alibaba_trace_replay_min_jct.log
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--csv_file_name=alibaba_trace_replay_min_jct.csv
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--log_level=debug
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# Workload configs.
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--execution_mode=replay
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--replay_trace=alibaba
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--workload_profile_paths=traces/alibaba-cluster-trace-v2018/easy_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/medium_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/hard_dag_sukrit_10k.pkl
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--workload_profile_path_labels=easy,medium,hard
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--override_release_policies=poisson,poisson,poisson
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--override_num_invocations=0,75,25
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--override_poisson_arrival_rates=0.0075,0.009,0.006
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--randomize_start_time_max=50
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--min_deadline=5
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--max_deadline=500
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--min_deadline_variances=25,50,10
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--max_deadline_variances=50,100,25
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# Worker configs.
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--worker_profile_path=./profiles/workers/alibaba_cluster.yaml
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# Loader configs.
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--alibaba_loader_task_cpu_divisor=10
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--alibaba_loader_min_critical_path_runtimes=200,500,600
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--alibaba_loader_max_critical_path_runtimes=500,1000,1000
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# Scheduler configs.
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--scheduler=GraphenePrime
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--scheduler_runtime=0
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--scheduler_selective_rescheduling
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--scheduler_selective_rescheduling_sample_size=2
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--scheduler_time_discretization=2
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--scheduler_enable_optimization_pass
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--scheduler_plan_ahead=1000
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--random_seed=420665456
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--retract_schedules
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--scheduler_time_limit=180
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# Output configs.
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--log_dir=experiments/alibaba_easy_medium_hard_20_slots/tetrisched/arrival_rate_0.009_0.006/
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--log_file_name=alibaba_trace_replay_tetrisched.log
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--csv_file_name=alibaba_trace_replay_tetrisched.csv
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--log_level=debug
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# Workload configs.
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--execution_mode=replay
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--replay_trace=alibaba
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--workload_profile_paths=traces/alibaba-cluster-trace-v2018/easy_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/medium_dag_sukrit_10k.pkl,traces/alibaba-cluster-trace-v2018/hard_dag_sukrit_10k.pkl
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--workload_profile_path_labels=easy,medium,hard
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--override_release_policies=poisson,poisson,poisson
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--override_num_invocations=0,75,25
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--override_poisson_arrival_rates=0.0075,0.009,0.006
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--randomize_start_time_max=50
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--min_deadline=5
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--max_deadline=500
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--min_deadline_variances=25,50,10
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--max_deadline_variances=50,100,25
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# Worker configs.
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--worker_profile_path=./profiles/workers/alibaba_cluster.yaml
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# Loader configs.
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--alibaba_loader_task_cpu_divisor=10
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--alibaba_loader_min_critical_path_runtimes=200,500,600
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--alibaba_loader_max_critical_path_runtimes=500,1000,1000
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# Scheduler configs.
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--scheduler=TetriSched
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--scheduler_runtime=0
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--enforce_deadlines
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--scheduler_time_discretization=1
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--scheduler_enable_optimization_pass
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--random_seed=420665456
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--retract_schedules
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--scheduler_time_limit=60

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