An Expert’s Guide to Orchestrating Microservices on AWS

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With an increased need for scalability and shorter time to market, enterprises have shifted to using microservice architectures for their businesses to have an edge in the market. Microservices continue to be a popular way of dividing up the functionality of complex systems. They offer flexibility to users that allows them to hone and scale on specific capabilities or features while following an agile delivery methodology.

Despite the great benefits that microservice architecture offers, it also adds intricacy, which can be challenging to manage. Microservices are undoubtedly efficient in scaling and utilization of resources, along with aligning your services with business domains. However, deploying and monitoring Microservices on cluster hosts or checking resource utilization gets progressively tricky when done manually.

Orchestrators come in handy in such situations, especially for production-ready applications that are based on microservices. Some of the most reliable and convenient options that inherently support the implementation of microservices are Docker with Swarm mode, Mesosphere’s DCOS, and frameworks like Akka.io, Spring cloud, Service fabric, etc.

Key considerations while selecting an option to manage and handle microservices include cluster management, monitoring, and scheduling services, auto-scaling, and service discovery. Let’s have a look at the most preferred approaches like Kubernetes, AWS EC2 Container Service (ECS), and Lambda functions for deploying and orchestrating Microservices over Amazon Web Services (AWS).

Dockerized Microservices on AWS EC2 Container Service (ECS)

Amazon EC2 Container Service (ECS) is quite a popular choice for many enterprises as it supports Docker containers and allows the user to efficiently run applications on a managed cluster of Amazon EC2 instances. ECS promotes the operation of containers across various availability zone within a specified region. It eliminates the need for you to install, manage, and scale your cluster management infrastructure.

Using simple API calls, you can launch and stop Docker-enabled applications, query the state of a particular cluster, and access many features like EBS volumes, security groups, elastic load balancing, and IAM roles. The task definitions enable the user to specify which Docker container images have to be run across the clusters. You can also integrate your customized scheduler or third-party schedulers to meet business or application-specific requirements.

Key Advantages

Automates monitoring of cluster management and services (Set of ECS tasks) using CloudWatch
Offloads intricate cluster management and orchestration of containerized Microservices
Provides enhanced scalability; auto-scales tasks (set of running containers as a unit) in an ECS service
It auto-scales AWS EC2 instances in the ECS cluster

Drawbacks

It requires custom implementation for automatic service discovery using CloudWatch, CloudTrail, and Lambda function
The feature of task-level auto-scaling is available only in selected regions. In such regions, a custom solution needs to be developed using SNS, CloudWatch, and AWS Lambda.
Vendor Lock-in

Kubernetes for EC2 cluster Dockerized Microservices

Kubernetes is an open-source orchestrator used for deploying containerized applications (microservices). It can also be defined as a platform for creating, managing, and deploying various distributed applications that can be of varied sizes and shapes. It was originally developed by Google to deploy reliable, scalable systems in containers via application-oriented APIs.

Kubernetes is a great option for orchestrating microservices, not just for internet-scale companies but also for cloud-native enterprises. Kubernetes master node is responsible for monitoring and scheduling of slave nodes where “Pods” are hosted. Pods enable the user to specify Docker container images that need to run across clusters.

Key Advantages

It doesn’t require vendor-lock-in. The same solution can be applied on the premises as well as on the cloud.
Developers’ test environment can be created quickly in a cost-effective manner using Kubernetes clusters
Offloads complex orchestration of containerized microservices
It provides automatic service discovery via a set of Kube-DNS package
Monitors and auto-scales set of containers in Kubernetes (pods)

Drawbacks

In order to achieve auto-scaling of the EC2 cluster, custom implementation is required using CloudWatch metrics and Lambda function

Microservices are executed as AWS Lambda Using AWS API Gateway

Microservice infrastructure can be intimidating at times, especially if too many servers are required to interact and work cohesively in a microservice container. In such cases, AWS Lambda can come to the rescue as it allows you to extend other AWS services with custom logic, or even create your serverless backends.

AWS Lambda operates your code on high-availability, fault-tolerant infrastructure code and performs all the computer resources administrative tasks such as server and operating system maintenance, code and security patch deployment, automatic scaling and capacity provisioning, logging, and code monitoring.

Key Advantages

AWS Lambda is quite easy to build and deploy as it offers serverless backends, therefore no requirements of servers, deployments, or software installations of any kind
You pay only for the requests served the compute time required to run your code. The total billing is measured in increments of 100 milliseconds, making it a cost-effective and easy-to-use option that can scale from a few requests per day to thousands per second.
With Lambda, you can use any additional programming language to author your functions. It natively supports Java, Go, PowerShell, Node.js, C#, Ruby code, and Python.
It seamlessly deploys your code and does all the administration, maintenance, and security patches, and provides built-in logging and monitoring via CloudWatch.
Scales automatically on the basis of incoming requests

Drawbacks

It limits troubleshooting and debugging support
It has Vendor-lock in
The maximum allowed time for each call execution is 300 seconds.
AWS Lambda limits the amount of storage and computes resources one can use to store and run functions.

Conclusion

It is important to consider various factors such as business needs, feasibility, viability, budget constraints, etc while selecting an option for orchestrating microservices. Every enterprise has a different set of end goals, team efficiency, workload, and price model, which play a huge role in deciding which method of orchestration would work best for a particular microservice architecture. Currently, Kubernetes is the most popular and reliable tool for running dockerized microservices in production, followed by Swarm and AWS ECS.

While some businesses require an orchestration method that would allow them to efficiently manage heavy workloads across their services, others demand easy future migration and expandability of management. It all depends on the organizational needs and might differ from one enterprise to another.

Microservice architectures offer a gamut of benefits that can ease up complex processes, increase scalability, reduce costs and churn, efficiently manage and operate a large volume of workloads in an errorless manner. But, they also tend to add complexity to the landscape of code, which means it is essential to select the right orchestration tool that meets your business-specific demands in an orderly manner without any hassle.

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