pydashsim - HAS simulation

Simulates a HTTP Adaptive Streaming (HAS) session based on a throughput pattern and video segment sizes. The simulation is part of the supplemental material to the HASBRAIN scientific paper: https://github.com/csieber/hasbrain

QUICKSTART

You need a traffic pattern and a video to run the simulation:

python3 pydashsim.py -t samples/traffictrace.csv -s samples/segments.csv

Select a specific adaptation logic with -l:

python3 pydashsim.py -l TRDA -t samples/traffictrace.csv -s samples/segments.csv

The simulation output files are placed in the out/ folder by default. Existing files are overwritten!

Adaptation Logics

Logic	Abbreviation	Description
NoAdaptationLogic (default)	NO	Always select lowest quality level.
KLUDCPLogic	KLUDCP	[1] by C. Müller et al.
TRDALogic	TRDA	[2] by K. Miller et al.
NeuralNetworkLogic	NN	[3] by C. Sieber et al.

Usage

usage: pydashsim.py [-h] [-o OPT_DIR] -t TRACE -s SEGMENTS [-l LOGIC] [-v]

Execute the simulation.

optional arguments:
  -h, --help            show this help message and exit
  -o OPT_DIR, --opt_dir OPT_DIR
                        Output folder
  -t TRACE, --trace TRACE
                        Goodput trace to use.
  -s SEGMENTS, --segments SEGMENTS
                        Video to use.
  -l LOGIC, --logic LOGIC
                        Adaptation logic (NO, KLUDCP, TRDA)
  -v, --verbose         Enable debug log.

Requirements

• scipy
• simpy
• keras (tested with 1.2)
• tensorflow

Citation

If you use the provided material, please cite the following paper:

Towards Machine Learning-Based Optimal HAS, Christian Christian, Korbinian Hagn, Christian Moldovan, Tobias Hoßfeld, Wolfgang Kellerer, August, 2018, Url: https://arxiv.org/abs/1808.08065

References

• [1] C. Müller, S. Lederer, and C. Timmerer, "An evaluation of dynamic adaptive streaming over http in vehicular environments,” in Proceedings of the 4th Workshop on Mobile Video. ACM, 2012.
• [2] K. Miller, E. Quacchio, G. Gennari, and A. Wolisz, “Adaptation algorithm for adaptive streaming over http,” in 19th International Packet Video Workshop (PV). IEEE, 2012.
• [3] C. Sieber, K. Hagn, C. Moldovan, T. Hoßfeld, W. Kellerer, Towards Machine Learning-Based Optimal HAS, August, 2018