Docker has revolutionized the IT industry by stepping in 2013 as a platform that uses OS-level virtualization to expose software applications to the internet. By leveraging the containerization model, the earlier problems faced by development and operations teams have been mitigated.

In the digital transformation era, we could see some formerly advanced methods and technologies are obsoleting. State-of-the-art & effective technologies are emerging as a better alternative in every aspect of life. In the same course, the containerized platforms are current & exciting technology that’s been able to answer all the queries while focusing on efficient cloud deployment and cost optimization. Docker helps you to create, deploy, and run applications in containers, which have all the dependencies within them. The container itself is a lightweight package that has all the instructions and dependencies such as frameworks, environment configuration, and libraries within it. With the containerization deployment model, now you can build and release complex applications faster and ship them to market without having dependencies issues.

A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another. The deployment manager or any other person who’s responsible for publishing services to the internet community. The person will be sure that a container has been built successfully in the local environment. Then, it must run successfully in any other containerized platform irrespective of which cloud provider’s services are being used. 

Cloud service providers like AWS, google with their serveless model made docker use even easier and more robust. You can scale your application at any level using on-demand resources offered by cloud service providers. Additionally, we are free to release the used resources anytime instead of making them a liability when they are no longer been used. 

The previously faced errors & omissions have been resolved with the containerization strategy. But still, many other new issues were approaching because of the high traffic-based apps being introduced in the market. These apps were associated with different industries which kept transforming the container management strategies. Due to the massive response to docker adoption, the new container orchestration tools became quickly famous in the software industry. 

Kubernetes(K8s) provided a vital breakthrough for the container-based model while providing appropriate solutions for auto-scaling, cost optimization, and security.

All the market-leading cloud platforms such as AWS, Azure, & Google have backed Kubernetes(K8s), and they tend to provide Kubernetes as a service. By getting inspired by its enormous adoption success, cloud platforms also moved toward introducing their K-native services like Amazon EKS, AKS, and GKE.

Amazon Elastic Container Service(ECS) is one of the most used and trusted platform-as-a-service(PaaS) provided by AWS. It is a managed container orchestration service that’s a K-native approach for a wide range of options for the end-user. It provides an efficient way to cater to the essential requirements of K-native environments as a Platform-as-a-service(PaaS). It provides networking solutions to make multi-tier applications secure from direct internet attacks. It provides a clustering approach for the required isolation between the environments. You can utilize the public and private subnets of the VPC for deploying the application stacks on it. It leverages the application load balancer(ALB) at the network level to expose the services safely by integrating the SSL for data encryption in the date transit phase. It provides the abstraction for internal docker configurations and offers a ready-to-use platform to deploy your monolithic, semi-monolithic, and microservices architecture on the ECS platform. AWS provides ECS Fargate to use the serverless approach and dilute server management for unusual and unprecedented traffic hikes with zero to thousands of containers in a few seconds.

Besides, Amazon Elastic Kubernetes Service(EKS) is an option to use while avoiding k8s configuration overheads, as it is a managed service by AWS. Hence, in EKS, you pay for Kubernetes as well. Therefore, the goal is to orchestrate the docker container within the infrastructure with a pay-as-you-go model, auto-scaling, and fault-tolerant reliable solution. The recommendation would be ECS due to its extensive features and support for serverless technology, auto-scaling mechanism, and cost optimization. But if you need more control over the k8s cluster configurations on the root level then EKS is recommended over ECS.

For demonstration, we’ll be working on Amazon ECS Fargate which is serverless technology having the capacity for scaling zero to thousands of containers in a few seconds:

Pre-Requisites:

• AWS Account
• Account Admin IAM Permissions

Let’s first configure & deploy an ECS Fargate cluster:

• Go to Elastic Container Service(ECS)

• When Clicking on the ‘Create Cluster’ button the following screen will come up.

• Give a name to the ECS cluster.

• Configure the network settings. If you have a custom VPC configure it in the networking and allocate the subnets where you want to provision the cluster. Otherwise, use the default VPC.

• By default, it is utilizing the Fargate Serverless Mode.

• Use container insights for ECS container-based monitoring.

• Click on create button.

• Now, Goto Task Definitions.

• Click on the ‘Create New Task Definition’ button.

• Name the task definition.

• Paste the Image URI here.

• Port mapping configurations of the image.

• Add the environment variables used if needed(optional).

• Click on the ‘next’ button. And you’ll see the following page:

• Use the Fargate capable environment as we are utilizing the ECS Fargate cluster.

• Task size configurations in terms of CPU and Memory consumption.

• The default network will be ‘awsvpc’ for Fargate. Set the Task execution role as ‘create new role’ if doesn’t exist before and provide the ‘Task Role’ if required.

• Use storage configurations as default(optional).

• Use the CloudWatch logging and monitoring. Enable ‘trace collection’ to integrate with AWS X-Ray for application traces.

• Click on the ‘next’ button.
•  Finally, review the task definition and click on the ‘create’ button.
• Go to the ECS cluster. Now, we’ll be deploying the ECS service on the cluster.

• Click on the ‘deploy’ button. Set the launch type as Fargate on the latest platform.

• In the deployment configurations, set the application type as service. The number of container tasks that must be running in the cluster should be given the desired tasks. Specify the task definition with respect to its revision.

• Set the deployment options and provide the deployment failure detection mechanism of ‘rollback on failure’ using the deployment service breaker.

• Click on the ‘Deploy’ button. The application must be deploy and healthy, you can review the stack in the ECS cluster services.

When it comes to the Google cloud platform, the GCP provides extensive support and integration with k8s like Google Kubernetes Engine(GKE), Cloud Run(Serverless), and GKE for Anthos for hybrid solutions. 

CloudRun is one of the best serverless services for running containers seamlessly. You just need to configure your docker containers & it works perfectly fine with the lift and shift model. It has a vast range of feature integrations and allows you to interact with a significant amount of services within the VPC to deploy your stack on it. CloudRun leverages the Kubernetes K-native underlying infrastructure for acquiring excellent service delivery with SLOs & SLAs.

Let’s discuss the application architecture that has been built and deployed on a similar sort of container platform. Starting from monolithic application architecture, where every piece of code is tightly coupled and has been written on a single stack. Monolith architecture mainly focuses on feature implementation for small-scale applications. It’s expected to be deployed as a singular service to achieve the goal.

As moving forward now, we are more familiar with multi-tier or multi-tenant application architecture. It may contain a frontend, backend, and database. Sometimes, it might use Redis for caching application data or a NoSQL database. These applications are developed on multiple stacks like Node, Python, Reactjs, etc. These kinds of applications architecture are more used in medium to large-scale applications.

Now, the fascinating application architecture is micro-services application architecture. It allows a substantial amount of advantages as compared to the other application architecture strategies. But it’s not recommended in all situations because it enhances the timeframe of the project feature release. The goal is to make code loosely coupled. So that each functionality can be referred to as an independent chunk of the application. Each functionality interacts with the other to achieve another bigger target of the application.

Moreover, the serverless-based infrastructure solutions seem to complement container orchestration in the best possible way. As it is easier to maintain and lift and shift strategy is need of the hour. Container-based solutions and their adoption in the industry of E-commerce, healthcare, fintech, retail, logistics, Artificial Intelligence(AI), and many other industries had proved their success and bright future ahead. The infrastructure abstraction would be necessary and manual intervention would be depreciated.

Nowadays, it’s becoming an IT industry standard to make your multi-tenant & multi-tier application stacks to be dockerized in the first place. Hence, it can value the exceptional benefits of horizontal and vertical scaling in the containerized platform on any public cloud such as GCP, AWS, Azure, etc.