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CARL SCHEDULER

Credits

  • Developers: The CARL Scheduler code was developed by Prathamesh (prathameshs@alum.iisc.ac.in) and Saswat from the Cloud Systems Lab (CSL) at IISc.

  • Please cite the following publication if you use CARL:

Prathamesh Saraf Vinayak; Saswat Subhajyoti Mallick; Lakshmi Jagarlamudi; Anirban Chakraborty; Yogesh Simmhan; CARL: Cost-Optimized Online Container Placement on VMs Using Adversarial Reinforcement Learning in IEEE Transactions on Cloud Computing, vol. 13, no. 1, pp. 321-335, Jan.-March 2025, doi: 10.1109/TCC.2025.3528446.

  • Code provided in this repository is a research prototype and provided as-is.

Installation

pip install -r requirements.txt

Note:

  • For vpsolver, you need to install gurobi and add it to your path.
  • For RL training, you need to install gym. Currently, gym has been upgraded to gymnasium, better to use that for speed and efficiency. Ideally port the code to gymnasium.
  • The way RL envs are registered and uses is different in the new gymnasium.
  • All the libraries such as sympy and all are upgraded, the current code is using the older versions. Best to port them to access the new features and stability.
  • I had to hack the library code for gym and RL to work.
  • Please try to port the code to latest sympy, gymnasium, imitation, stable-baselines3, etc.

Links:

Usage

  • Refer Makefile for evaluation
  • Simulator code is in Simulator/
  • RL training code is in RL/
  • You need to generate expert trajectories first, refer to generate_expert_traj_vp.py in RL/, need access to Gurobi
  • To train RL models, refer to commands.txt in RL/, installed libary versions require changes to the library code. Better to install newer libraries and make them work.