Today Aviatrix launched the self-paced version of Aviatrix Certified Engineer (ACE) Infrastructure as Code (IAC) training and certification. This is the industry’s first multi-cloud networking and security Infrastructure as Code training, that too in a self-paced format.
ACE IaC brings together the concepts of DevOps by automating a multi-cloud network infrastructure through 3 hands-on labs. The self-paced training guides students towards building complex networks with the Aviatrix Multi-Cloud Networking and Security platform by using the principles of DevOps and Infrastructure as Code.
Students can expect to enter the training with no coding background and complete the training with a solid understanding of how to use IaC tools (GitHub and Terraform Cloud) to build, enhance, and secure multi-cloud networks at scale in an automated fashion. They will learn how to collaborate with other stakeholders in their organization by building CI/CD pipelines to apply a very common use case in the cloud – Egress Security.
This post details my experience with creating a simple multi-tier Kubernetes app using Google Cloud Platform (GCP) as well as Amazon Web Services (AWS) by taking advantage of the free tier accounts for each service. The app comprises a Redis master for storage, multiple Redis read replicas (a.k.a Slaves), and load-balanced web frontends. Kubernetes acts as a Frontend load balancer that proxies traffic to one or more of these Slaves (known as ‘container nodes’ in Kubernetes lingo). In order to manage these, I created Kubernetes replication controllers, pods, and services in this sequence:
Create the Redis master replication controller.
Create the Redis master service.
Create the Redis slave replication controller.
Create the Redis slave service.
Create the guestbook replication controller.
Create the guestbook service.
GCP has a few easy-to-follow tutorials for their main services. For Google Kubernetes Engine (GKE), the tutorial walks you through creating cluster to deploy a simple Guestbook application to the cluster.
AWS launched Amazon Elastic Container Service for Kubernetes (Amazon EKS) at re:Invent 2017 and it became Generally Available in June 2018.
Installing the latest AWS CLI. On the other hand, Google Cloud Shell in GCP makes it really simple to perform operation and administration tasks.
Installing a tool to use AWS IAM credentials to authenticate to a Kubernetes cluster. It’s not clear to me why exactly this is needed. The documentation for AWS IAM Authenticator for Kubernetes states “If you are an administrator running a Kubernetes cluster on AWS, you already need to manage AWS IAM credentials to provision and update the cluster. By using AWS IAM Authenticator for Kubernetes, you avoid having to manage a separate credential for Kubernetes access.” However, it appears to be a needless step. Why can’t Kubernetes clusters in AWS EKS leverage AWS IAM credentials directly?
In GCP, Google Cloud Shell includes the kubectl CLI utility. In AWS, you need to install it locally. Moreover, GCP made it far easier to configure the kubeconfig file by issuing the gcloud container clusters get-credentials pakdude-kubernetes-cluster –zone us-central1-a command. With EKS, I had to manually edit the kubeconfig file to populate the cluster endpoint (called anAPI server endpoint in EKS) and auth data (called a Certificate Authority in EKS).
Creating a Kubernetes cluster takes about 10 minutes in EKS, compared to just 2-3 minutes with GKE.
When creating the Kubernetes cluster in EKS, as per the documentation, You must use IAM user credentials for this step, not root credentials. I got stuck on this step for a while, but it is good practice anyway to use an IAM user instead of the root credentials.
In GKE, I ran into memory allocation issues when creating node pools that had machine type f1.micro, which are 1 shared vCPU, 0.6 GB memory. However, when I created the node pools with machine type g1-small (1 shared vCPU, 1.7 GB memory), things ran more smoothly. In EKS, t1.micro instances are not even offered; the smallest type of instance I could specify for the NodeInstanceType was t2.small.
The supported Operating Systems for nodes in GKE node pools are Container Optimized OS (cos) and Ubuntu. In EKS, you have to use an Amazon EKS-optimized AMI, which differs across regions. That difference, alone, opens up inconsistencies between the two implementations.
There is very little one can do at the AWS Console for EKS. Most of the work at the CLI or programmatically. For example, there is no way in AWS to check the status of worker nodes. You have to use the kubectl get nodes –watch command.
The sample GKE Frontend/Guestbook code was cloned from Git Hub. After setting it up, it gave this output:
umairhoodbhoy@pakdude713:~$ kubectl get pods
NAME READY STATUS RESTARTS AGE
frontend-qgghb 1/1 Running 0 23h
frontend-qngcj 1/1 Running 0 23h
frontend-vm7nq 1/1 Running 0 23h
redis-master-j6wc9 1/1 Running 0 23h
redis-slave-plc59 1/1 Running 0 23h
redis-slave-r664d 1/1 Running 0 23h
umairhoodbhoy@pakdude713:~$ kubectl get rc
NAME DESIRED CURRENT READY AGE
frontend 3 3 3 23h
redis-master 1 1 1 1d
redis-slave 2 2 2 23h
umairhoodbhoy@pakdude713:~$ kubectl get services
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
frontend LoadBalancer 10.11.242.118 126.96.36.199 80:30549/TCP 23h
kubernetes ClusterIP 10.11.240.1 <none> 443/TCP 1d
redis-master ClusterIP 10.11.241.226 <none> 6379/TCP 1d
redis-slave ClusterIP 10.11.246.51 <none> 6379/TCP 23h
The sample EKS Guestbook app was also cloned from Git Hub. After setting it up, it gave this output:
Obviously, these are just simple Guestbook applications and I only covered the ease of setting up Kubernetes. A more relevant measure of Kubernetes on either cloud platform would be the performance and scalability. I had no easy way of stress testing the apps. However, after going through this exercise in both AWS and GCP, it is clear where GCP’s strengths lie. AWS may be the dominant Public Cloud player, but launching EKS despite having its own Elastic Container Service (ECS) is an indication of how popular Kubernetes is as a container orchestration system. Running Kubernetes on AWS is incredibly cumbersome compared to running it on GCP. EKS is relatively new and I’m sure AWS will iron out the kinks in the months to come.
This post details my experience with creating a simple two-tier app using Google Cloud Platform (GCP) as well as Amazon Web Services (AWS) by taking advantage of the free tier accounts for each service.
GCP has a few easy-to-follow tutorials for their main services. For Google Compute Engine (GCE), the tutorial walks you through creating a two-tier app in which the Frontend VM is a Node.js ToDo Web app and the Backend VM runs MongoDB. For either service, I followed these steps:
Create and configure VMs in GCE or AWS Elastic Cloud Compute (EC2)
Connect via SSH to each VM to install appropriate packages and run the relevant services.
In GCP, I started by creating each VM instance separately.
In AWS, however, EC2 let me specify 2 instances to create simultaneously with the identical settings other than the IP addresses.
First, I created the Backend VM that ran MongoDB. The only customizations required were to select a Micro instance (to incur the fewest charges on my Free Tier account), specifying Ubuntu 14.04 LTS as the OS, and opening up HTTP for the Frontend and Backend VMS to communicate. In AWS, I did this by applying a Security Group to the instances that opened up port 80.
Next, I created the Frontend VM that runs the Node.js ToDo application with the same customizations that I applied for the Backend VM.
Once both VM instances were created, I connected to them via SSH.
GCP offers a built-in browser-based terminal utility called Cloud Shell as an alternative to Terminal (Mac OS) or PuTTY (Windows).
Then, I installed and ran the Frontend web app by cloning the sample application. The sample application exists in a GCP repository on Github and I used it for the GCP experiment as well as the AWS one:
It sure has been a while since I’ve written here. Lately I’ve been experimenting with the free-tier accounts for both Amazon Web Services (AWS) and Google Cloud Platform. I’ve been trying to achieve the same things on both platforms and will be sharing my experiences in subsequent posts. I hope you can stick around to read them!