Multi-Cloud Setup of Kubernetes

Task Description 📄

📌 Create a Multi-Cloud Setup of K8S Cluster:

🔅 Launch node in AWS

🔅 Launch node in Azure

🔅 Launch node in GCP

🔅 And one over network on local system /Cloud → Master Node

🔅 Then setup multi node Kubernetes cluster.

What is Kubernetes?

Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications. Kubernetes is an open-source container-orchestration system for automating computer application deployment, scaling, and management. It was originally designed by Google and is now maintained by the Cloud Native Computing Foundation.

As applications grow to span multiple containers deployed across multiple servers, operating them becomes more complex. To manage this complexity, Kubernetes provides an open source API that controls how and where those containers will run.

Kubernetes orchestrates clusters of virtual machines and schedules containers to run on those virtual machines based on their available compute resources and the resource requirements of each container. Containers are grouped into pods, the basic operational unit for Kubernetes and those pods scale to your desired state.

Kubernetes also automatically manages service discovery, incorporates load balancing, tracks resource allocation and scales based on computing utilization. And, it checks the health of individual resources and enables apps to self-heal by automatically restarting or replicating containers.

Kubernetes uses Docker for creating container.

What is Docker?

Docker is a container management service. The keywords of Docker are develop, ship and run anywhere. The whole idea of Docker is for developers to easily develop applications, ship them into containers which can then be deployed anywhere.

To know the actual ground details about containers and docker: Containers: The actual mechanism behind the technology and why Kubernetes depreciated Docker | by Gursimar Singh | May, 2021 | Medium

What is a Multi-Node cluster in Kubernetes?

A Kubernetes cluster is a set of node machines for running containerized applications. If you’re running Kubernetes , you’re running a cluster. At a minimum, a cluster contains a control plane and one or more compute machines, or nodes . Nodes actually run the applications and workloads.

A multi-node cluster in Kubernetes is a setup with various nodes among which one is known as the master node and the rest are the worker nodes.

For more details about Multi-Node Cluster of Kubernetes visit: Ansible Role to Configure Kubernetes Multi Node Cluster over AWS Cloud | by Gursimar Singh | Apr, 2021 | Medium

AWS (Amazon Web Services)

AWS is one of the biggest cloud providers with supports various technologies. Provides services for building, testing, monitoring, deploying, and running the whole business on the cloud. They also support technologies like Augmented Reality, Virtual Reality, quantum technologies, robotics, etc.

One of the services is EC2 (Elastic Cloud Computing). Here they provide virtual machines with the support of many major operating systems and resources such as ram, CPU, networking, etc.

We will configure the Kubernetes cluster over EC2 instances.

GCP(Google Cloud Platform)

Google Cloud consists of a set of physical assets, such as computers and hard disk drives, and virtual resources, such as virtual machines (VMs), that are contained in Google’s data centers around the globe. Each data center location is in a region. Regions are available in Asia, Australia, Europe, North America, and South America. Each region is a collection of zones, which are isolated from each other within the region. Each zone is identified by a name that combines a letter identifier with the name of the region.

This distribution of resources provides several benefits, including redundancy in case of failure and reduced latency by locating resources closer to clients. This distribution also introduces some rules about how resources can be used together.

Microsoft Azure

The Azure cloud platform is more than 200 products and cloud services designed to help you bring new solutions to life — to solve today’s challenges and create the future. Build, run and manage applications across multiple clouds, on-premises and at the edge, with the tools and frameworks of your choice.

Ansible

Ansible is an open-source automation platform. It is a simple automation language that can perfectly describe an IT application infrastructure in Ansible Playbooks. It is also an automation engine that runs Ansible Playbooks.

Ansible can manage powerful automation tasks and can adapt to different workflows and environments. At the same time, new users of Ansible can very quickly use it to become more productive.

Let’s jump into the task,

First, let us launch an EC2 instance. We will be configuring it as our master node.

Below are the services we need to launch on AWS using Ansible

Create a VPC (Virtual Private Cloud)

Create subnets in that VPC.

Create an internet gateway.

Create routing table.

Create an internet gateway.

Creating security group.

Launch ec2-instances in that subnet of respective VPC.

                Creating VPC

- name: VPC for EC2
    ec2_vpc_net:
      aws_access_key: "{{ aws_access_key }}"
      aws_secret_key: "{{ aws_secret_key }}"
      name: "{{ vpc_name }}"
      cidr_block: "{{ vpcCidrBlock }}"
      region: "{{ region }}"
      # enable dns support
      dns_support: yes
      # enable dns hostnames
      dns_hostnames: yes
      tenancy: default
      state: "{{ state }}"
    register: ec2_vpc_net_result
Creating subnets in the VPC.

- name: Subnet for VPC
    ec2_vpc_subnet:
      aws_access_key: "{{ aws_access_key }}"
      aws_secret_key: "{{ aws_secret_key }}"
      vpc_id: "{{ ec2_vpc_net_result.vpc.id }}"
      region: "{{ region }}"
      az: "{{ zone }}"      # az is the availability zone
      state: "{{ state }}"
      cidr: "{{ subNetCidrBlock }}"
      # enable public ip
      map_public: yes
      resource_tags:
        Name: "{{ subnet_name }}"
    register: subnet_result
Creating internet gateway

# create an internet gateway for the vpc
  - name: create ec2 vpc internet gateway
    ec2_vpc_igw:
      aws_access_key: "{{ aws_access_key }}"
      aws_secret_key: "{{ aws_secret_key }}"
      vpc_id: "{{ ec2_vpc_net_result.vpc.id }}"
      region: "{{ region }}"
      state: "{{ state }}"
      tags:
        Name: "{{ igw_name }}"
    register: igw_result
Creating routing table

- name: Creating routing table for EC2 VPC Public Subnet
    ec2_vpc_route_table:
      aws_access_key: "{{ aws_access_key }}"
      aws_secret_key: "{{ aws_secret_key }}"
      vpc_id: "{{ ec2_vpc_net_result.vpc.id }}"
      region: "{{ region }}"
      state: "{{ state }}"
      tags:
        Name: "{{ route_table_name }}"
      subnets: [ "{{ subnet_result.subnet.id }}" ]# create routes
      routes:
        - dest: "{{ destinationCidrBlock }}"
          gateway_id: "{{ igw_result.gateway_id }}"
    register: public_route_table
Creating security group

- ec2_group:
      aws_access_key: "{{ aws_access_key }}"
      aws_secret_key: "{{ aws_secret_key }}"
      vpc_id: "{{ ec2_vpc_net_result.vpc.id }}"
      region: "{{ region }}"
      state: "{{ state }}"
      name: "{{ security_group_name }}"
      description: "{{ security_group_name }}"
      tags:
        Name: "{{ security_group_name  }}"
      rules:
      - proto: all
        cidr_ip: "{{ port22CidrBlock }}"
        rule_desc: allow all traffic
    register: security_group_results
Launching EC2-instance

- name: "Provisioning OS on AWS using Ansible"
  ec2:
     key_name: "ansiblekey"
     instance_type: "t2.micro"
     image: "ami-08e0ca9924195beba"
     wait: yes
     count: 1
     vpc_subnet_id: "{{ subnet_result.subnet.id }}"
     assign_public_ip: yes
     region: "ap-south-1"
     state: present
     group_id: "{{ security_group_results.group_id }}"
     aws_access_key: "{{aws_access_key}}"
     aws_secret_key: "{{aws_secret_key}}"
     instance_tags:
             Name: "{{ item }}"
  loop: "{{ OS_Names }}"
Vars file of EC2 playbook :
---# vars file for EC2-launch 
image: "ami-089c6f2e3866f0f14"
instance_type: "t2.micro"
region: "us-east-2"
key: testingkey
vpc_subnet_id: "subnet-2321516f"
security_group_id: "sg-07a58bacace819405"
OS_Names:        
    - "K8S_Master"        
aws_access_key: 'xxxxxxxxxxxxxx'
aws_secret_key: 'xxxxxxxxxxxxxxxxxxxxxxxxxx'

We can confirm that the instance has launched after running the playbook by checking the AWS console.

Now let us configure it as our master node for the kubernetes cluster.

Steps to be performed in all the nodes.

First, install docker and start the services. For setting up Kubernetes need a docker driver with systemd. By default, systemd commands are not supported by containers.
Configure the systemd to the docker.
Configuring Kubernetes repository for yum.
Install Kubectl, Kubelete, and kubeadm.

Kubectl: The Kubernetes command-line tool, kubectl, allows you to run commands against Kubernetes clusters.

Kubelet: An agent that runs on each node in the cluster. It makes sure that containers are running in a Pod.

The kubelet takes a set of PodSpecs that are provided through various mechanisms and ensures that the containers described in those PodSpecs are running and healthy. The kubelet doesn’t manage containers that were not created by Kubernetes.

Kubeadm: It is a Kubernetes cluster management tool. It performs the necessary actions to create a Kubernetes cluster. It also useful for upgrading, joining multiple nodes, manages Kubernetes certificates, external authentications for the cluster.

Now we can start kubelet services
You also need to install iproute-tc to maintain the network inside Kubernetes cluster.

iproute-tc:- The Traffic Control utility manages queueing disciplines, their classes, and attached filters and actions. It is the standard tool to configure QoS in Linux. Simply it manages the network traffic in the cluster.

                - name: Install Docker
  package:
    name: “docker”
    state: present
- name: Changing docker driver
  copy:
    src: daemon.json
    dest: “/etc/docker/daemon.json”
- name: Starting Docekr services
  service:
    name: docker
    state: restarted
    enabled: yes
- name: Configuring Kubernetes Repository
  copy:
    dest: “/etc/yum.repos.d/kubernetes.repo”
    src: kubernetes.repo
- name: Installing the k8s applications
  command: “yum install -y kubelet kubeadm kubectl — disableexcludes=kubernetes”
- name: Starting kubelet services
  service:
    name: “kubelet”
    state: restarted

- name: Installing iproute-tc
  package:
     name: ‘iproute-tc’
     state: present
- name: Pulling images
    shell: kubeadm config images pull
- name: Configuring network
    shell: “echo ‘1’ > /proc/sys/net/bridge/bridge-nf-call-iptables”

The above steps are common for the master and worker nodes, as you move forward you can see the differences.

Setting up the Master Node

Config Kubernetes admin file
Starting the kubeadm init services
kubeadm generates a token, which helps the worker node to connect with the master node.
Configure the flannel with Kubernetes

Flannel is a virtual networking layer designed specifically for containers. This helps the pods and nodes to keep connected to the master node, it will configure automatically to every worker node.

                - name: Initializing Kubeadm Servicessetup
  command: kubeadm init — pod-network-cidr=10.240.0.0/16 — ignore-preflight-errors=NumCPU — ignore-preflight-errors=Mem
  ignore_errors: true
- name: Creating .kube directory
  file:
    path: ~/.kube
    state: directory
    mode: 0755
- name: link the admin.conf with .kube/admin file
  file:
    src: /etc/kubernetes/admin.conf
    dest: ~/.kube/config
    state: link
    mode: 0644
- name: Generating a token
  command: kubeadm token create — print-join-command
  register: token
- name: Set the kubeadm join command globally
 set_fact:
    kubernetes_join_command: >
     {{ token.stdout }}
 when: token.stdout is defined
 delegate_to: “{{ item }}”
 delegate_facts: true
 with_items: “{{ groups[‘all’] }}”
- name: Transfering network file
  copy:
    src: kube-flannel.yml
    dest: /root/kube-flannel.yml
- name: Creating an Overlay Network to connect worker nodes
  command: kubectl apply -f /root/kube-flannel.yml

That’s it we are done with setting up the master node.

Now, let us set up the worker nodes and connect them to complete the multi-cloud m cluster

First let us set up a virtual instance on Microsoft Azure,

Here, first we need to create a resource group
Then we need to create a virtual network
After that we need to add subnet
Then we create public IP address
We will be enabling SSH in order to configure it with ansible later on
We need to create virtual network interface card
Finally we create the VM

                # This playbook create an Azure VM with public IP, and open 22 port for SSH, and add ssh public key to the VM.
# This playbook create an Azure VM with public IP
# Change variables below to customize your VM deployment
- name: Create Azure VM
  hosts: localhost
  connection: local
  vars:
    resource_group: "{{ resource_group_name }}"
    vm_name: testvm
    location: eastus
    ssh_key: "<KEY>"
  tasks:
  - name: Create a resource group
    azure_rm_resourcegroup:
        name: "{{ resource_group }}"
        location: "{{ location }}"
  - name: Create virtual network
    azure_rm_virtualnetwork:
      resource_group: "{{ resource_group }}"
      name: "{{ vm_name }}"
      address_prefixes: "10.0.0.0/16"
  - name: Add subnet
    azure_rm_subnet:
      resource_group: "{{ resource_group }}"
      name: "{{ vm_name }}"
      address_prefix: "10.0.1.0/24"
      virtual_network: "{{ vm_name }}"
  - name: Create public IP address
    azure_rm_publicipaddress:
      resource_group: "{{ resource_group }}"
      allocation_method: Static
      name: "{{ vm_name }}"
  - name: Create Network Security Group that allows SSH
    azure_rm_securitygroup:
      resource_group: "{{ resource_group }}"
      name: "{{ vm_name }}"
      rules:
        - name: SSH
          protocol: Tcp
          destination_port_range: 22
          access: Allow
          priority: 1001
          direction: Inbound
  - name: Create virtual network interface card
    azure_rm_networkinterface:
      resource_group: "{{ resource_group }}"
      name: "{{ vm_name }}"
      virtual_network: "{{ vm_name }}"
      subnet: "{{ vm_name }}"
      public_ip_name: "{{ vm_name }}"
      security_group: "{{ vm_name }}"
- name: Create VM
    azure_rm_virtualmachine:
      resource_group: "{{ resource_group }}"
      name: "{{ vm_name }}"
      vm_size: Standard_DS1_v2
      admin_username: azureuser
      ssh_password_enabled: false
      ssh_public_keys:
        - path: /home/azureuser/.ssh/authorized_keys
          key_data: "{{ ssh_key }}"
      network_interfaces: "{{ vm_name }}"
      image:
        offer: CentOS
        publisher: OpenLogic
        sku: 7.5
        version: latest

This will launch a CentOS VM.

Now let us launch a VM over GCP,

First we need to create a compute disk
Then we need to create an address (this is for the IP address)
Finally we configure and create the instance

                - name: Create an instance
  hosts: localhost
  gather_facts: no
  vars:
      gcp_project: my-project
      gcp_cred_kind: serviceaccount
      gcp_cred_file: /home/my_account.json
      zone: "us-central1-a"
      region: "us-central1"
tasks:
   - name: create a disk
     gcp_compute_disk:
         name: 'disk-instance'
         size_gb: 50
         source_image: 'projects/ubuntu-os-cloud/global/images/family/ubuntu-1604-lts'
         zone: "{{ zone }}"
         project: "{{ gcp_project }}"
         auth_kind: "{{ gcp_cred_kind }}"
         service_account_file: "{{ gcp_cred_file }}"
         scopes:
           - https://www.googleapis.com/auth/compute
         state: present
     register: disk
   - name: create a address
     gcp_compute_address:
         name: 'address-instance'
         region: "{{ region }}"
         project: "{{ gcp_project }}"
         auth_kind: "{{ gcp_cred_kind }}"
         service_account_file: "{{ gcp_cred_file }}"
         scopes:
           - https://www.googleapis.com/auth/compute
         state: present
     register: address
   - name: create a instance
     gcp_compute_instance:
         state: present
         name: test-vm
         machine_type: n1-standard-1
         disks:
           - auto_delete: true
             boot: true
             source: "{{ disk }}"
         network_interfaces:
             - network: null # use default
               access_configs:
                 - name: 'External NAT'
                   nat_ip: "{{ address }}"
                   type: 'ONE_TO_ONE_NAT'
         zone: "{{ zone }}"
         project: "{{ gcp_project }}"
         auth_kind: "{{ gcp_cred_kind }}"
         service_account_file: "{{ gcp_cred_file }}"
         scopes:
           - https://www.googleapis.com/auth/compute
     register: instance
- name: Wait for SSH to come up
     wait_for: host={{ address.address }} port=22 delay=10 timeout=60
- name: Add host to groupname
     add_host: hostname={{ address.address }} groupname=new_instances

We are done with launching the instances now we need to install docker and kubernetes as mentioned above.

Setting up as Worker Nodes

Just run the kubeadm join command with the token generated in the master

                - name: connecting to the master node  shell: >  {{ kubernetes_join_command }}

That’s it we are done it setting up the cluster.

To check everything is working fine

Now, let’s check the status of the cluster by logging in to our EC2 master node.

                The Kubelet service is active and running. ($ Systemctl status kubelet)

Docker is also active and running. ($ Systemctl status docker)
To check the status of the pods,

$ kubectl get nodes
We can create an application of our choice and deploy it in the Kubernetes cluster.

                #deploy the app 
$ kubectl create deployment myapp --image=vimal13/apache-webserver-php
#expose the deployment to the real world
$ kubectl expose service myapp --port=80 --type=NodePort
#We will get a link to connect to the application

We also can perform the above steps in the GUI console in a similar fashion for a better understanding of the process.

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Gursimar Singh

Software Engineer Intern, Kickback Space

@gursimar

An undergraduate scholar in DevOps, Cloud Computing, Machine Learning, Big Data, and more. Quantum Computing Enthusiast and a Proficient Photographer.