zhangyi19941217/iroko

A platform to test reinforcement learning policies in the datacenter setting.

Iroko: The Data Center RL Gym

Iroko is an open source project that is focused on providing openAI compliant gyms. The aim is to develop machine learning algorithms that address data center problems and to fairly evaluate solutions again traditional techniques.

A more concrete description is available in our short paper.

Requirements

The data center emulator makes heavy uses of Linux tooling and its networking features. It operates most reliably on a recent Linux kernel (4.15+). The supported platform is Ubuntu (at least 16.04 is required). Using the emulator requires full sudo access.

Package Dependencies

• GCC or Clang and the build-essentials are required.
• git for version control
• libnl-route-3-dev to compile the traffic managers
• bwn-ng and ifstat to monitor traffic
• python and python-setuptools to build Python packages and run the emulator

Python Dependencies

The generator supports both Python2 and Python3. pip and pip3 can be used to install the packages.

• numpy for matrix operations
• gym to install openAI gym
• seaborn and matplotlib to generate plots

Mininet Dependencies

The datacenter networks are emulated using Mininet. At minimum Mininet requires the installation of

• openvswitch-switch, cgroup-bin, help2man

Ray Dependencies

The emulator uses Ray to implement and evaluate reinforcement learning algorithms. Ray's dependencies include:

• tensorflow, setproctitle, psutil, opencv-python

Goben Dependencies

The emulator generates and measures traffic using Goben. While an amd64 binary is already provided in the repository, the generator submodule can also be compiled using Go 1.11. The contrib/ folder contains a script to install Goben locally.

Installation

A convenient, self-contained way to install the emulator is to run the ./install.sh. It will install most dependencies locally via Poetry.