PiTree

PiTree is a conversion tool to automatically and faithfully convert complex adaptive bitrate algorithms into lightweight decision trees. This repository is the official release of the following paper:

Zili Meng, Jing Chen, Yaning Guo, Chen Sun, Hongxin Hu, Mingwei Xu. PiTree: Practical Implementations of ABR Algorithms Using Decision Trees. In Proceedings of ACM Multimedia 2019.

For more information, please refer to https://transys.io/pitree.

Prerequisites

Tested with Python 3.7.4:

pip install -r requirements.txt
unzip traces.zip
unzip models.zip
mkdir results
mkdir tree

Converting Decision Trees

Pre-built ABR Algorithms: RobustMPC, Pensieve, and HotDASH

python learn_dt.py -a pensieve -t fcc -i 500 -n 100 -q lin

Parameter | Candidates | Explanation :-: | :-: | :-: -a | {robustmpc, pensieve, hotdash} | The ABR algorithm to convert. -i | Integer (default=500) | Number of iterations during training. -n | Integer (default=100) | Number of leaf nodes. -q | {lin, log, hd} | QoE metrics. -t | {fcc, norway, oboe} | Trained traces. -v | {0,1} | Visualized the output decision tree. -w | Integer (default=1) | Degree of parallelism of teacher.predict().

The converted decision tree could be found at tree/, in the pickle format.

Add Your Own ABR Algorithms

If you want to test your own ABR algorithms with PiTree, you could

• Expose the predict function of your methods in the format of $a=f(s)$.
• Put your model into models/ (if any).
• Add your methods into the interfaces defined in learn_dt.py.

(We will refactor the codes soon in a more user-friendly way and will update the repo soon.)

Simulation with Pensieve Simulator

python main.py -a pensieve -t fcc -q lin -d path/to/your/tree.pk -l

Parameter | Candidates | Explanation :-: | :-: | :-: -a | {robustmpc, pensieve, hotdash} | The ABR algorithm to convert. -d | {0,1} | Predict with the decision tree (1) or the original model (0). -l | {0,1} | Log the states and bitrates. -q | {lin, log, hd} | QoE metrics. -t | {fcc, norway, oboe} | Trained traces.

Put the Decision Tree into HTML and Deploy with Apache

Currently, you may want to refer to this link for details. We will refactor this part soon.

Start a Server with Tornado

python server_tornado.py

Citation

@inproceedings{meng2019pitree,
 author = {Meng, Zili and Chen, Jing and Guo, Yaning and Sun, Chen and Hu, Hongxin and Xu, Mingwei},
 title = {PiTree: Practical Implementation of ABR Algorithms Using Decision Trees},
 year = {2019},
 url = {https://doi.org/10.1145/3343031.3350866},
 booktitle = {Proceedings of the 27th ACM International Conference on Multimedia},
 pages = {2431–2439},
 series = {MM ’19}
}

Contact

For any questions, please post an issue or send an email to zilim@ieee.org.