sagkit 0.0.16

A toolkit for constructing and analyzing schedule-abstraction graph in Python

SAGkit

A lightweight Python toolkit for response-time analysis based on schedule-abstraction graph. You may also be interested in Official SAG repository (in C++), sag-go (in GO), and sag-py (in Python).

Install

You may install SAGkit with pip:

pip install sagkit

or reproduce the results in our paper with a docker image (requires Docker):

docker pull caoruide/sagkit

Use

The jobset generator takes the following arguments:

• --ET_ratio: What percentage of jobs are ET. Default is 15.
• --utilization: What percentage of the macrocycle is the expectation of the total execution time. Default is 45.
• --jobset_folder: Which folder to save the jobsets. Default is "./jobsets/".
• --num_job: How many jobs to include in each job set. Default is 1000.
• --num_instance: How many jobsets to generate for each set of parameter combinations. Default is 1.

Generate jobsets:

python -m sagkit.jobset_generator [ET_ratio] [utilization] [jobset_folder] [num_job] [num_instance]

The SAG constructor takes the following arguments:

• --jobset_folder: Which folder to read the jobsets. Default is "./jobsets/".
• --constructor_type: What constructor(s) to use to do the construction. Default is "original,extended,hybrid".
• --save_dot: Which folder to save the dot files. Default is "./dotfiles/".
• --save_statistics: Which path to save the statistics results. Default is "./statistics.csv".

Construct SAGs:

python -m sagkit.sag_constructor [jobset_folder] [constructor_type] [save_dot] [save_statistics]

Example Usage (Manually type in 1 jobset)

1. Create a folder /example/ in the current working directory.

2. Create a file 'example.txt' in the /example/ folder.

3. Write the following content (Fig. 4 example in our paper) into example.txt.

   0 2 9 10 20 1 1
   1 2 5 6 25 4 0
   4 5 1 2 25 3 0
   3 6 2 3 25 2 0
   

   or write any job you want for each line, in the following format:

   BCAT WCAT BCET WCET deadline priority ET
   

   • BCAT (Best-Case Arrival Time): Earliest arrival time for the job.
   • WCAT (Worst-Case Arrival Time): Latest arrival time for the job.
   • BCET (Best-Case Execution Time): Minimum time required for the job to be executed.
   • WCET (Worst-Case Execution Time): Maximum time required for the work to be executed.
   • deadline: Deadline in absolute time.
   • priority: A smaller value implies higher priority.
   • ET (Event-Triggered): Whether the job is potentially absent, with 0 being impossible and 1 being possible.

4. Go back to the original working directory. Run the constructor:

   python -m sagkit.sag_constructor --jobset_folder ./example/
   

5. The constructed SAGs will be saved in ./dotfiles/ folder. To visualize, paste the contents of each .dot file to:

   https://dreampuf.github.io/GraphvizOnline (you may want to access in incognito mode.)
   

Example Usage (Automatically generate 84 jobsets)

1. Generate jobsets:

   python -m sagkit.jobset_generator --ET_ratio 0,10,15,20,30,40,50,60,70,80,90,100 --utilization 45,50,55,60,65,70,75
   

2. Construct SAGs (29 hours on my computer):

   python -m sagkit.sag_constructor --jobset_folder ./jobsets/ --save_statistics ./statistics.csv
   

3. View the statistics in ./statistics.csv.

Reproduce

• Step 1: Create a folder named 'results' in the current working directory:

mkdir results

• Step 2: Reproduce Fig. 1 and 2. The .dot files corresponding to the figures ('original.dot' for Fig.1, 'hybrid.dot' for Fig.2) will appear under the /results folder after running the following command:

docker run -v "$(pwd)/results:/output" caoruide/sagkit sagkit.sag_constructor --save_dot True --jobset_folder /basic_idea/

• Step 3 (optional): Visualize Fig. 1 and 2. This step is optional because the .dot files are readable and easy to understand. Paste the contents of each .dot file to:

https://dreampuf.github.io/GraphvizOnline (you may want to access in incognito mode.)

• Step 4: Reproduce Fig. 3. Also, you may visualize the .dot files ('original.dot' for Fig. 3 (a), 'extended.dot' for Fig. 3 (b), 'hybrid.dot' for Fig. 3 (c)) following Step 3.

docker run -v "$(pwd)/results:/output" caoruide/sagkit sagkit.sag_constructor --save_dot True --jobset_folder /example1/

• Step 5: Reproduce Fig. 4. Also, you may visualize the .dot files ('original.dot' for Fig. 4 (a), 'extended.dot' for Fig. 4 (b), 'hybrid.dot' for Fig. 4 (c)) following Step 3.

docker run -v "$(pwd)/results:/output" caoruide/sagkit sagkit.sag_constructor --save_dot True --jobset_folder /example2/

• Step 6: Generate job sets:

docker run -v "$(pwd)/results:/output" caoruide/sagkit sagkit.jobset_generator --ET_ratio 0,10,15,20,30,40,50,60,70,80,90,100 --utilization 45,50,55,60,65,70,75    

• Step 7: Reproduce Fig. 5 and Tables 2-4. This step may take some time (29 hours on my machine). All numerical results corresponding to Figure 5 and Tables 2-4 will be automatically generated in the /results/statistics.csv file. The construction times may vary from Fig. 5 (a), (b), (c) depending on the computational power of the machine.

docker run -v "$(pwd)/results:/output" caoruide/sagkit sagkit.sag_constructor  

Test

Install SAGkit from source:

git clone https://github.com/RyderCRD/sagkit

Change direcotry to ./sagkit (over the /src directory):

cd sagkit

Run all unit tests:

python -m unittest discover tests

Contribute

Contributions are welcome! Please feel free to drop your issues and PRs :)