The dag-gen-rnd is a randomised multiple Directed Acyclic Graph (DAG) task generator designed for scheduling and allocation research in parallel and multi-core computing.
dag-gen-rnd supports both command line (daggen-cli) and graphical user interface (daggen-gui; in development). This generator can be easily configured through a .json file and is highly extensible for other purposes.
Support the following DAG generation algorithms:
nfj: Nested fork-joinrnd: standard randomised DAG (layer-by-layer; LBL)rnd_legacy: default randomised DAG
Supported utilisation generation algorithms:
UUnifast[1]UUnifast-discard[2]
References:
[1] Bini, Enrico, and Giorgio C. Buttazzo. "Measuring the performance of schedulability tests." Real-Time Systems 30, no. 1 (2005): 129-154.
[2] Davis, Robert I., and Alan Burns. "Improved priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems." Real-Time Systems 47, no. 1 (2011): 1-40.
[3] Griffin, David, Iain Bate, and Robert I. Davis. "Generating utilization vectors for the systematic evaluation of schedulability tests." In 2020 IEEE Real-Time Systems Symposium (RTSS), pp. 76-88. IEEE, 2020.
Python >= 3.7NetworkX >= 2.4Matplotlib >= 3.1.3pygraphviz >= 1.5numpy >= 1.17tqdm >= 4.45.0pyqt5(optional for GUI)
Install dependencies using apt:
$ sudo apt install python3-dev graphviz libgraphviz-dev pkg-config
and then install Python dependencies through requirements.txt:
$ pip3 install -r requirements.txt
(Optional) To use the GUI, you need to install Qt5 for Python:
$ sudo apt install python3-pyqt5
Use the configuration file config.json to configure parameters.
To generate a single DAG task, set multi-DAG=false, then in single_task:
multi-DAG: falseset_number: number of tasksetsworkload: sum(C_i)
To generate a multi-DAG taskset, set multi-DAG=true, then in multi_task:
set_number: number of tasksetsutilization: total utilisationtask_number_per_set: number of tasks in each tasksetperiods: period set candidates
First, change the configurations in config.json. Then, run the command line interface:
$ python3 src/daggen-cli.py
$ python3 src/daggen-gui.py
To use the generated DAGs, see the provided API in utlity.py which also gives an example.
Here are some simple examples of generated DAGs:
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or more complicated DAGs that can also be generated:
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- Compatibility on Windows: This code is tested on Linux (Ubuntu) but not on Windows. There should not be too many problems as Python is good at cross-platform. However, the only potential issue is that the difference is in folder naming, where Windows uses a backslash (
\) instead of a forward slash (/). I will test it and make it compatible in the future. - In some cases, the workload of the critical path could be larger than the period. The generator does not prohibit this case as this is not treated as a bug (as you can distribute the workload to multiple cores). The users need to be awareof this and deal with them in their favourite way, e.g. discarding.
- If you get an error while building pygraphviz during installing the dependencies, install graphviz with
apt install graphviz graphviz-dev.
The generator is featured in multiple academic papers:
- Ping, Yani, Kun Xie, Xiaohong Huang, Chengcheng Li, and Yasheng Zhang. "GNN-Based QoE Optimization for Dependent Task Scheduling in Edge-Cloud Computing Network." In 2024 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6. IEEE, 2024.
- Zhang, Yuanhai, Shuai Zhao, Gang Chen, and Kai Huang. "Fault-tolerant DAG scheduling with runtime reconfiguration on multicore real-time systems." In 2024 IEEE 35th International Conference on Application-specific Systems, Architectures and Processors (ASAP), pp. 19-27. IEEE, 2024.
- Kang, Hong, Minghao Li, Lehao Lin, Sizheng Fan, and Wei Cai. "Bridging Incentives and Dependencies: An Iterative Combinatorial Auction Approach to Dependency-Aware Offloading in Mobile Edge Computing." IEEE Transactions on Mobile Computing, 2024.
- Cucinotta, Tommaso, Alexandre Amory, Gabriele Ara, Francesco Paladino, and Marco Di Natale. "Multi-criteria optimization of real-time DAGs on heterogeneous platforms under p-EDF." ACM Transactions on Embedded Computing Systems 23, no. 1, 2024.
- Shuai Zhao, Xiaotian Dai, Iain Bate, Alan Burns, Wanli Chang. "DAG scheduling and analysis on multiprocessor systems: Exploitation of parallelism and dependency." In Real-Time Systems Symposium (RTSS), pp. 128-140. IEEE, 2020.
- Shuai Zhao, Xiaotian Dai, Iain Bate. "DAG Scheduling and Analysis on Multi-core Systems by Modelling Parallelism and Dependency." Transactions on Parallel and Distributed Systems (TPDS). IEEE, 2022.
Please cite the following paper if you use this software in your research:
Shuai Zhao, Xiaotian Dai, Iain Bate, Alan Burns, Wanli Chang. "DAG scheduling and analysis on multiprocessor systems: Exploitation of parallelism and dependency". In Real-Time Systems Symposium (RTSS), pp. 128-140. IEEE, 2020.
BibTex:
@inproceedings{zhao2020dag,
title={DAG scheduling and analysis on multiprocessor systems: Exploitation of parallelism and dependency},
author={Zhao, Shuai and Dai, Xiaotian and Bate, Iain and Burns, Alan and Chang, Wanli},
booktitle={2020 IEEE Real-Time Systems Symposium (RTSS)},
pages={128--140},
year={2020},
organization={IEEE}
}
Alternatively, if you want to cite the software:
Xiaotian Dai. (2022). dag-gen-rnd: A randomised multi-DAG task generator for scheduling and allocation research (v0.1). Zenodo. https://doi.org/10.5281/zenodo.6334205
BibTex:
@software{xiaotian_dai_2022_6334205,
author = {Xiaotian Dai},
title = {{dag-gen-rnd: A randomised multi-DAG task generator
for scheduling and allocation research}},
month = mar,
year = 2022,
publisher = {Zenodo},
version = {v0.1},
doi = {10.5281/zenodo.6334205},
url = {https://doi.org/10.5281/zenodo.6334205}
}
This software is licensed under MIT. See LICENSE for details.
