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https://github.com/bytedance/drl-based-vm-rescheduling

This repo contains the implementation of deep reinforcement learning (DRL) algorithms for virtual machine rescheduling in data centers.

https://github.com/bytedance/drl-based-vm-rescheduling

Science Score: 26.0%

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    Low similarity (6.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

This repo contains the implementation of deep reinforcement learning (DRL) algorithms for virtual machine rescheduling in data centers.

Basic Info
  • Host: GitHub
  • Owner: bytedance
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 300 KB
Statistics
  • Stars: 10
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Archived
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme Contributing License

README.md

Deep Reinforcement Learning-based Virtual Machine Rescheduling

We are still working on this repository. A more complete and clean version will be provided soon.

Installation Steps

  1. Install Anaconda:

$ conda create -n rl_vm_scheduling python=3.7 $ conda activate rl_vm_scheduling

  1. Install RLlib:

$ pip install gym==0.23.1 $ pip install "ray[rllib]" tensorflow torch $ pip install -e gym-reschdule_combination

Running Steps

  • Train PPO-based agent $ python3 main.py
  • To use pretrained model for VM selection $ python3 main.py --track --model [mlp/attn] --pretrain
  • Evaluation $ python3 eval.py --restore-name [] --restore-file-name [] --model [mlp/attn]

Environments

  • generalizer-v0: Base environment. Fixed number of VMs.
  • generalizer-v1: Dynamic number of VMs.
  • graph-v1: Dynamic number of VMs with vm-pm affiliations to support graph models.

Owner

  • Name: Bytedance Inc.
  • Login: bytedance
  • Kind: organization
  • Location: Singapore

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Dependencies

gym-reschdule_combination/gym_reschdule_combination.egg-info/requires.txt pypi
  • gym *
gym-reschdule_combination/setup.py pypi
  • gym *