Our project modifies a kubernetes deployment to modify workloads to take into consideration the energy budget. The goal is to build Energy Aware Datacenter and ultimately reduce energy consumption to help build a greener planet.
We use Kubernetes, a technology that is lighter weight than a virtual machine while also providing greater bin packing even as it shrinks an application to ensure successful deployments. We also use Golang, Terraform, and React in building a Kubernetes energy-aware datacenter in GCP.
- Grafana - UI that queries the data saved in Prometheus using PromQL and displays the change in CPU usage, number of pods running, dynamically with graphs and visualization.
- Energy Signal Panel - UI for admin to input an external energy signal, check the current status of the signal and view the number of pods running inside Kubernetes
- kubectl - Command line tool that enables us to deploy and manage workloads in kubernetes.
- Cloud Load Balancing - Load Balancer that redirects external traffic to specific services running inside Kubernetes.
- Prometheus - Time series database that pulls the data from metrics server and saves in JSON format
- Monitor - Internal monitor that fetches data & updates the cluster
- Policy - the system’s energy signal receiver
- Power - the system’s energy signal responder
- Kube API - API allows the users, different parts of the system communicate with each other
- Metrics - Server that stores information about the cluster such as CPU and Memory usages of pods running inside the cluster.
- Node - A physical or virtual machine that can run multiple pods.
- Pod - A smallest execution unit inside kubernetes. It contains a collection of container images and a specific IP address.
- Container Registry - A registry to store container images in the cloud.
- Kube-scheduler Manager - Watches for newly created pods and makes sure that pods are matched to nodes.
- Cloud-controller Manager - Kubernetes control plane component that embeds cloud specific control logic. It lets the user link the cluster into the cloud provider’s API.
- Kube-controller Manager - A daemon that embeds the core control loops shipped with Kubernetes.
This document shows you how to re-build the whole project.
Our system runs on a Kubernetes cluster, the first step is to deploy a cluster on Google Kubernetes Engine.
Terraform is an open-source infrastructure as a code software tool to provision data center infrastructure. For installation, please refer to install-guide
terraform/cluster/directory contains the terraform code to provision a GKE cluster.- Create a service account under
IAM & Adminfor our GCP project if not already created. - Fetch the secret-key and store it as
tf-key.jsonunderterraform/cluster/.
terraform initterraform planterraform apply
The easiest way to authenticate is by using gcloud. For gcloud installation, please refer to install-guide
- Under GKE cluster, click on the connect button and copy-past
gcloud <command>and run it in your local terminal. kubectl config current-contextto cross-reference to currently deployed GKE cluster.kubectl get pods -Ato display the pods as a sanity check.
- Under GKE cluster, copy & paste the certificate file and store it as
ca.pemunderprod/
Deploying apps via kubectl:
app/directory containsdeployment.yaml,service.yaml,ingress.yaml.
kubectl apply -f deployment.yamlkubectl apply -f service.yamlkubectl apply -f ingress.yaml
In the energy-aware datacenter, Zeus is responsible for Policy, e.g. receiving energy signals from client, maintaining Policy.
- For binary, run:
make zeus - For docker image, run:
docker build --tag <tag> . - You are supposed to see the built image when run:
docker images - To run it:
docker run -d -it -p 8080:9999 --name zeus -v my-vol:/app <tag> --rm - And now you can access it with http://localhost:8080 on your browser
- Tunnel the docker-env to Minikube:
eval $(minikube -p minikube docker-env) - Build the image into minikube's docker:
docker build --tag <tag> . - Create Deployment:
kubectl create -f deployment.yml - Create Service:
kubectl expose deployment zeus --type=LoadBalancer --port=9090 - Check out the service:
minikube service zeus
- configure your Docker with gcloud:
gcloud auth configure-docker - Push the image to GCR(Google Cloud Registry):
docker push us.gcr.io/kube-flux/kube-flux-zeus:0.0.3 - Deploy the container:
kubectl apply -f low.yaml kubectl apply -f medium.yaml kubectl apply -f top.yamlkubectl create deployment test --image=us.gcr.io/kube-flux/kube-flux-zeus:0.0.3 - Expose the Service:
kubectl expose deployment zeus --name=zeus-service --type=LoadBalancer --port 80 --target-port 9999 - Now you'd see the external Ip by calling
kubectl get service!
- Enter the backend directory:
cd final/main/ - Run
go run main.go $PEMPATH <CLUSTER_IP_ADDRESS> <NAMESPACE>
- Enter the frontend directory:
cd frontend - Install dependencies:
npm install - Run app and open http://localhost:9000 to view it in the browser:
npm start
brew install helmhelm repo add prometheus-community https://prometheus-community.github.io/helm-chartshelm repo add stable https://kubernetes-charts.storage.googleapis.com/helm repo updatekubectl create namespace monitoringhelm install prometheus prometheus-community/kube-prometheus-stack —version "10.1.1" —namespace monitoringkubectl port-forward deployment/prometheus-grafana -n monitoring 3000
If you can help us with these. Please don't hesitate to open a pull request.
 Lacee Xu |
 Siddhartha Chowdhuri |
 Sum Xu |
 Tong Wang |