Learning-Augmented Dynamic Power Management

This repository contains source code accompanying paper Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds by Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon.

Dependencies

You need Python 3 with additional packages: matplotlib, networkx, numpy. You also need pdflatex in order to produce the plots.

To install them using pip on Ubuntu run:

sudo apt install python3-pip texlive-latex-base
pip3 install matplotlib networkx numpy

Synthetic datasets

We run our experiments on synthetic randomly generated instances. The instances we used are available in data/ folder. If you want to sample your own instances, run:

python3 build_datasets.py

Synthetic experiments

To generate the plots present in the paper, simply run the script:

./make_plots.sh

That should take about 9 hours on a modern 8-core CPU. You can change -n 10 to -n 1 in make_plots.sh file to run one instead of ten repetitions of each experiment and get preliminary results quicker.

The plots are generated in the pdf format, and the raw data is stored in json files. The results we obtained are stored in the results/ folder.

Real-world datasets and experiments

Our real-world experiments are based on traces available at iotta.snia.org. The preprocessed traces that we used are availale in data/folder. If you want to recreate the instances, first, download Nexus5_Kernel_BIOTracer_traces.tar.gz from http://iotta.snia.org/traces/block-io. Then, run:

./build_nexus_datasets.sh

To run experiments and generate the plots, run:

./make_nexus_plots.txt