DRS

A Deep Reinforcement Learning enhanced Kubernetes Scheduler for Microservice-based System

File description

• scheduler/ : The source code of kube-scheduler for Kubernetes (version 1.23.4). The DRS scheduler is in registed in scheduler/framework/plugins/dqn/dqn.go

• deploy/ :

  • apps/ : the application configure file and deploy script.

    The docker images of the applications in available on DockerHub.

  Application	Type	Description	Docker Image
  Video Scale	CPU-intensive	Scale the video to a certain size with ffmpeg	jolyonjian/apps:cpu-1.0
  Transmission	Network-intensive	Transfer data of a certain size to the server	jolyonjian/apps:net-1.0
  Data Write	IO-intensive	Read a file on the disk and write a copy	jolyonjian/apps:io-1.0

  • scripts/ : Scripts for cluster creation, initialization, deletion, etc.

• drs-scheduler/ : DRS scheduler runs on the master node of the k8s cluster

• drs-monitor/ : DRS monitor runs on the worker node of the k8s cluster

Run

1. Modify the source code of Kubernetes (version 1.23.4) to regist DRS scheduler and compile the project.

# Configure go environment

# clone the source code of Kubernetes v1.23.4
$ git clone -b v1.23.4 https://github.com/kubernetes/kubernetes.git
$ mv ./kubernetes <path of go-workspace>/

$ git clone https://github.com/JolyonJian/DRS
$ cd DRS
# Replace the source code of Kube-scheduler
$ mv ./scheduler <path of go-workspace>/kubernetes/pkg/
$ cd <path of go-workspace>/kubernetes
$ make

# After the first compilation you can only compile the kube-scheduler
$ make cmd/kube-scheduler

2. Initalize the Kubernetes cluster.

# Start a k8s cluster (on the master node)
$ cd <path of DRS>/deploy/scripts
$ ./init.sh
$ ./env.sh

# Add the worker nodes into the cluster (on each worker node)
$ kubeadm join <mater-node-ip>:6443 --token <your-token> --discovery-token-ca-cert-hash <your-cert-hash>

# Deploy the network and the second scheduler plugins
$ cd <path of DRS>/deploy/apps
$ ./apply.sh kube-flannel.yaml
$ ./apply.sh drs-scheduler.yaml

3. Start the DRS scheduler and DRS the monitor.

# Start the DRS scheduler (on the master node)
$ cd <path of DRS>/scheduler
# The node ip needs to be configured according to your environment
$ python dqn.py

# Start the DRS monitor (on each worker node)
$ cd <path of DRS>/monitor
# The node ip and port need to be configured according to your environment
$ ./monitor.sh

4. Deploy applications to the cluster.

$ cd <path of DRS>/deploy/apps
# Specify the scheduler in the configuration file
$ ./apply.sh <app.yaml>

Contact

The link of our paper (Under Review): https://www.authorea.com/doi/full/10.22541/au.167285897.72278925

If you have any questions, please contact us.

Zhaolong Jian: jianzhaolong@mail.nankai.edu.cn