Enterprises are under pressure to revamp their IT systems and curtail operational costs. Traditional virtual machine-based architectures have limitations and pose a challenge to the digital IT ecosystems, especially in the context of microservices. While Virtual Machines (VMs) have been a significant part of IT infrastructure for years, their overhead costs and lack of agility hinder the seamless deployment and scaling of microservices-based applications. Consider today’s scenario where 92% of global enterprises are running at least one production workload with a cloud hyperscaler, VM-based infrastructures still dominate. That’s where container-first cloud modernization comes into the picture. Containerization and cloud-native applications bring in much-needed flexibility and cost optimization.
Let’s look at the transformational merits of container-first cloud modernization and its impact on organizations.
Defining a container-first approach
A container-first approach involves developing or refactoring applications to run in docker containers. These lightweight containers contain everything necessary for an application to run: source code, runtime, system tools, dependencies, external libraries, and configurations. Once packaged as a container, applications can run universally, regardless of computer architecture or operating systems. Often coupled with cloud-native services, this approach amplifies its advantages.
Limitations of traditional VM-Based Architecture
To put things into perspective, let’s first understand the limitations of the traditional VM-based architecture, especially for microservices.
- Resource Intensive: Each VM runs a full copy of an operating system, which can lead to wasted resources. This is particularly problematic for microservices, which are meant to be lightweight and efficient.
- Slow Start-up Times: VMs can take a significant amount of time to boot up, which can slow down the deployment of microservices and affect the organization’s agility.
- Less Portability: VMs are less portable than containers as they are closely tied to the underlying host OS. This can create challenges when moving applications between different environments.
Let’s look at the advantages.
Merits of a container-first approach
- Improved portability: Containers encapsulate all dependencies, ensuring applications run consistently across different computing environments. This portability simplifies moving applications between environments, from development to production or from on-premises infrastructure to the cloud.
- Enhanced scalability: Containers can be rapidly started, stopped, and replicated. This makes it easier to scale applications in response to demand. With cloud-based container orchestration tools like Kubernetes, automation helps in scaling applications.
- Increased efficiency: Containers are more resource-efficient than traditional virtual machines, as they share the host system’s OS kernel instead of demanding the entire OS for each instance. This means significant savings in system resources, which can reduce costs and improve performance.
- Faster deployment cycles: The container-first approach can streamline the software development lifecycle. Containers support CI/CD pipelines, enabling faster, more reliable deployments. With this approach, organizations respond more swiftly to market changes and customer demands.
Kubernetes, an open-source platform designed to automate deploying, scaling, and operating application containers, has become the de facto standard for container orchestration. Kubernetes offers a framework to run distributed systems resiliently, thereby scaling and healing applications as needed.
Taking a comparative view
For this comparison, consider AWS as the cloud provider and an online retailer’s e-commerce website as the reference workload. The following comparisons aim to mimic the hosting in an Elastic Cloud Compute (EC2) infrastructure vis-à-vis Elastic Kubernetes Service (EKS), a managed Kubernetes engine.
Every cloud hyperscaler has similar capabilities, and the following comparison is valid for equivalent services across all cloud hyperscalers:
| Features | A comparative view of two services | |
| Amazon EC2 | Amazon EKS | |
| Infrastructure setup | · Requires provisioning and managing EC2 instances with specific resource allocations (CPU, RAM, storage).[1] · Each application typically runs on a separate EC2 instance, leading to potential resource wastage due to over-provisioning | · Utilizes managed Kubernetes service (EKS) for container orchestration. [2] · Containers run on worker nodes within an EKS cluster, sharing the underlying EC2 instances for improved resource utilization. |
| Deployment flexibility | · Deployment involves spinning up new EC2 instances for each application, which can be time-consuming and resource-intensive. | · Deployment is streamlined through container images managed by Kubernetes, enabling rapid scaling and deployment of applications with minimal overhead. |
| Scalability | · Scaling requires provisioning additional EC2 instances, which may lead to underutilization during periods of low traffic and potential over-provisioning during peak times. | · Offers horizontal scaling by dynamically scaling the number of pods (containers) based on demand, ensuring optimal resource utilization and cost efficiency. |
| Cost analysis | · Costs include EC2 instance provisioning fees, typically charged based on resource allocation (CPU, RAM, storage) and uptime. [3] · Additional costs may arise from software licenses for operating systems and applications installed on each EC2 instance. | · Costs primarily consist of AWS EKS service fees and underlying EC2 instance costs for worker nodes in the EKS cluster. [4] · Containerization minimizes overhead, potentially leading to cost savings compared to EC2 instances, especially in scenarios with fluctuating workloads. |
| Operational overheads | · Management involves monitoring and maintaining individual EC2 instances, including patching, updates, and security configurations. | · Simplifies operations with centralized management through Kubernetes, automating tasks like scaling, load balancing, and service discovery [5]. |
| Total cost of ownership | · TCO will be higher due to potential inefficiencies in resource utilization, higher infrastructure costs, and increased operational overheads. | · TCO tends to be lower due to improved resource utilization, streamlined operations, and potential cost savings from utilizing managed Kubernetes service for container management. |
To sum up, while both EC2-based and EKS container-based architectures have merits, containerization with EKS offers a significant advantage in terms of cost efficiency, scalability, and operational simplicity in the example of a retail e-commerce company—hence, it is a compelling choice for modernizing IT infrastructure on AWS.
Final thoughts
A container-first cloud modernization approach offers numerous benefits for organizations, including improved portability, enhanced scalability, increased efficiency, and faster deployment cycles. By adopting this approach and leveraging tools like Kubernetes, organizations can overcome the limitations of traditional VM-based architectures, optimize costs, and position themselves for success in the digital age.
Contact us to know how we can help you with this approach.
References:
- [1] AWS EC2 Instance Pricing: https://aws.amazon.com/ec2/pricing/
- [2] AWS EKS Pricing: https://aws.amazon.com/eks/pricing/
- [3] AWS EC2 Pricing Details: https://aws.amazon.com/ec2/pricing/details/
- [4] AWS EKS Pricing Details: https://aws.amazon.com/eks/pricing/details/
- [5] Kubernetes Documentation: https://kubernetes.io/docs/home/
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Author’s background and expertise
Pallab Chatterjee, a Senior Director and Enterprise Solution Architect at Movate, leads the cloud practices division. Having worked in many different industries and countries for more than 16 years, he is an expert Multi-Cloud Specialist. Pallab is an expert at orchestrating successful migrations of over 25 workloads across major cloud hyperscalers. He is an expert in edge computing, big data, security, and the Internet of Things. Notably, he has designed over ten innovative use cases in edge computing, IoT, AI/ML, and data analytics, establishing his standing as a forerunner in the tech industry. LinkedIn