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Adding Alibaba configs from erdos3.
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sukritkalra committed Sep 19, 2024
1 parent c68c352 commit 592dc23
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38 changes: 38 additions & 0 deletions configs/alibaba_easy_medium_hard_dagsched.conf
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# Output configs.
--log_dir=experiments/alibaba_easy_medium_hard_20_slots/dagsched/arrival_rate_0.009_0.006/
--log_file_name=alibaba_trace_replay_dagsched.log
--csv_file_name=alibaba_trace_replay_dagsched.csv
--log_level=debug

# Workload configs.
--execution_mode=replay
--replay_trace=alibaba
--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
--workload_profile_path_labels=easy,medium,hard
--override_release_policies=poisson,poisson,poisson
--override_num_invocations=0,75,25
--override_poisson_arrival_rates=0.0075,0.009,0.006
--randomize_start_time_max=50
--min_deadline=5
--max_deadline=500
--min_deadline_variances=25,50,10
--max_deadline_variances=50,100,25

# Worker configs.
--worker_profile_path=./profiles/workers/alibaba_cluster.yaml

# Loader configs.
--alibaba_loader_task_cpu_divisor=10
--alibaba_loader_min_critical_path_runtimes=200,500,600
--alibaba_loader_max_critical_path_runtimes=500,1000,1000

# Scheduler configs.
--scheduler=TetriSched
--release_taskgraphs
--scheduler_runtime=0
--enforce_deadlines
--scheduler_time_discretization=1
--scheduler_enable_optimization_pass
--random_seed=420665456
--retract_schedules
--scheduler_time_limit=180
33 changes: 33 additions & 0 deletions configs/alibaba_easy_medium_hard_edf.conf
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# Output configs.
--log_dir=experiments/alibaba_easy_medium_hard_20_slots/edf/arrival_rate_0.009_0.006/
--log_file_name=alibaba_trace_replay_edf.log
--csv_file_name=alibaba_trace_replay_edf.csv
--log_level=debug

# Workload configs.
--execution_mode=replay
--replay_trace=alibaba
--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
--workload_profile_path_labels=easy,medium,hard
--override_release_policies=poisson,poisson,poisson
--override_num_invocations=0,75,25
--override_poisson_arrival_rates=0.0075,0.009,0.006
--randomize_start_time_max=50
--min_deadline=5
--max_deadline=500
--min_deadline_variances=25,50,10
--max_deadline_variances=50,100,25

# Worker configs.
--worker_profile_path=./profiles/workers/alibaba_cluster.yaml

# Loader configs.
--alibaba_loader_task_cpu_divisor=10
--alibaba_loader_min_critical_path_runtimes=200,500,600
--alibaba_loader_max_critical_path_runtimes=500,1000,1000

# Scheduler configs.
--scheduler=EDF
--scheduler_runtime=0
--enforce_deadlines
--random_seed=420665456
39 changes: 39 additions & 0 deletions configs/alibaba_easy_medium_hard_min_jct.conf
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# Output configs.
--log_dir=experiments/alibaba_easy_medium_hard_20_slots/min_jct/arrival_rate_0.009_0.006/
--log_file_name=alibaba_trace_replay_min_jct.log
--csv_file_name=alibaba_trace_replay_min_jct.csv
--log_level=debug

# Workload configs.
--execution_mode=replay
--replay_trace=alibaba
--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
--workload_profile_path_labels=easy,medium,hard
--override_release_policies=poisson,poisson,poisson
--override_num_invocations=0,75,25
--override_poisson_arrival_rates=0.0075,0.009,0.006
--randomize_start_time_max=50
--min_deadline=5
--max_deadline=500
--min_deadline_variances=25,50,10
--max_deadline_variances=50,100,25

# Worker configs.
--worker_profile_path=./profiles/workers/alibaba_cluster.yaml

# Loader configs.
--alibaba_loader_task_cpu_divisor=10
--alibaba_loader_min_critical_path_runtimes=200,500,600
--alibaba_loader_max_critical_path_runtimes=500,1000,1000

# Scheduler configs.
--scheduler=GraphenePrime
--scheduler_runtime=0
--scheduler_selective_rescheduling
--scheduler_selective_rescheduling_sample_size=2
--scheduler_time_discretization=2
--scheduler_enable_optimization_pass
--scheduler_plan_ahead=1000
--random_seed=420665456
--retract_schedules
--scheduler_time_limit=180
37 changes: 37 additions & 0 deletions configs/alibaba_easy_medium_hard_tetrisched.conf
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# Output configs.
--log_dir=experiments/alibaba_easy_medium_hard_20_slots/tetrisched/arrival_rate_0.009_0.006/
--log_file_name=alibaba_trace_replay_tetrisched.log
--csv_file_name=alibaba_trace_replay_tetrisched.csv
--log_level=debug

# Workload configs.
--execution_mode=replay
--replay_trace=alibaba
--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
--workload_profile_path_labels=easy,medium,hard
--override_release_policies=poisson,poisson,poisson
--override_num_invocations=0,75,25
--override_poisson_arrival_rates=0.0075,0.009,0.006
--randomize_start_time_max=50
--min_deadline=5
--max_deadline=500
--min_deadline_variances=25,50,10
--max_deadline_variances=50,100,25

# Worker configs.
--worker_profile_path=./profiles/workers/alibaba_cluster.yaml

# Loader configs.
--alibaba_loader_task_cpu_divisor=10
--alibaba_loader_min_critical_path_runtimes=200,500,600
--alibaba_loader_max_critical_path_runtimes=500,1000,1000

# Scheduler configs.
--scheduler=TetriSched
--scheduler_runtime=0
--enforce_deadlines
--scheduler_time_discretization=1
--scheduler_enable_optimization_pass
--random_seed=420665456
--retract_schedules
--scheduler_time_limit=60

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