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Software for creating simulation scenarios for online and reservation-based batch schedulers used in large-scale computing.

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Simulator for HPC schedulers

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The ScheduleFlow software consists of a series of scripts and classes that offer an API allowing users to create simulation scenarios for online and reservation-based batch schedulers.

Citation

If you use the package available here in your work, please refer to the latest release.

DOI

Documentation

For details on the API or the internals of the Simulator, visit the wiki

Getting Started

Clone the repository or download the latest release:

git clone https://github.com/anagainaru/ScheduleFlow.git ScheduleFlow

In the python code import the ScheduleFlow package:

sys.path.append("path/to/ScheduleFlow")
import ScheduleFlow

For generating GIF animations, the path to the ScheduleFlow root directory needs to be saved in the ScheduleFlow_PATH environmental variable, either in the python code:

import os
os.environ["ScheduleFlow_PATH"] = "path/to/ScheduleFlow"

or as linux command:

export ScheduleFlow_PATH="path/to/ScheduleFlow"

Requirements

The ScheduleFlow package was build for python 3 and requires numpy. For running on python 2, enum34 needs to be installed.

Generating GIFs for simulations requires pdflatex and convert from ImageMagick.

Example

python run_simple_example.py

* Tested with python 3.7

Crash course

The script simulates the execution of 11 jobs submitted to a reservation-based scheduler and an online scheduler.

The simulation assumes a system of 10 processing units.

The workload used consists of:

  • 10 jobs with randon execution times (requesting larger execution times than needed) and random processing unit requirements.
  • one large job running on almost the entire machine and for large units of time (equesting less time than it needs to complete successfully)

The simulation can check for correctness, generate an animation of the scheduling process and output the results to a file or to stdout. Declaring a new Simulation object:

simulator = Simulator.Simulator(check_correctness=True,
                                generate_gif=True,
                                output_file_handler=sys.stdout)

To start a simulation a scenario needs to be created for a given simulation:

scheduler = Scheduler.BatchScheduler(system)
simulator.create_scenario(scenario_name,
                          scheduler,
                          job_list=job_list)
simulator.run()

The scenario uses a scheduler and a list of jobs that need to be simulated. In addition, jobs can be added to a scenario by using the simulator.add_applications(job_list) method. Alternatively, the run_scenario method can be called directly on the simulator:

simulator.run_scenario(scenario_name, scheduler, job_list)

The scenario name is used to create the animation GIF filenames and for debugging purposes. The resubmit factor indicates that failed jobs need to be resubmitted with an execution time increase given by the facor. By default, failed jobs are not resubmitted.

The GIF generation is controled by the VizEngine class. It uses the tex_header and tex_footer files in the ./draw directory to generate tex files for every step of the animation. Pdflatex is used fo create PDF files which are used by ImageMagick to generate the GIF.

Output

If successfuly ran, the run_simple_example.py script will output*:

Scenario name : job failures : job response time : job stretch : job utilization : job wait time : system makespan : system utilization :
test_batch : 1.00 : 361.64 : 5.78 : 0.73 : 259.08 : 606.00 : 0.67 :
test_online : 1.00 : 374.91 : 6.86 : 0.73 : 271.25 : 553.00 : 0.74 :
GIFs generated in ./draw/test_{batch, online}.gif

Example GIFs generated:

Reservation-based scheduler simulation*

Batch scheduler

Online scheduler simulation*

Online scheduler

* Depeding on the requirements of the jobs, the scheduler might give other execution orders for the submitted jobs and thus slightly different performance values

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Software for creating simulation scenarios for online and reservation-based batch schedulers used in large-scale computing.

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