Java is one of the most prominent languages in the global enterprise software development ecosystem. In fact, over 90% of Fortune 500 businesses depend on Java for their software development efforts. However, many of these Java applications exist as large faded monolithic systems running on legacy infrastructure, burdened by years of technical debt. With many organizations accelerating their digital transformations, changing legacy applications from ‘past due’ to ‘overdue’ will no longer be optional. The change is in fact necessary for organizational agility, scalability, and resilience.

AWS provides an array of tools and services to help organizations modernize their Java applications at scale. In this blog post, we will provide five practical methods to modernize Java workloads on AWS using established patterns and AWS-native solutions. These methods allow organizations to innovate with confidence, deliver on their necessary risk management, and maintain business continuity during technology transformations.

Containerization: Lift-and-Shift for Quick Wins

This is one of the efficient ways to modernize Java applications. Containerization is packaging the application (along with its dependencies) into containers.

Why does it work?

• Portability: Containers abstract Java applications from the host OS, meaning that the application should run the same way regardless of environment.
• Timely Deployment: You can go from hours to minutes by deploying on immutable infrastructure.
• Resource utilization: Containers enable higher density on compute instances than traditional virtual machines.

AWS tools:

• Use Amazon EKS (Elastic Kubernetes Service) and ECS (Elastic Container Service) for completely managed orchestration services.

• For analysis and containerization of legacy Java applications with minimal changes to the code use AWS App2Container (A2C).

• AWS Fargate is a serverless compute engine for containers, which eliminates the need to manage server infrastructure.

Best practice:

Containerize non-critical services first, then migrate the rest progressively. Make sure to add observability with Amazon CloudWatch and tracing of the application performance within containers using AWS X-Ray.

Refactor to Microservices with Spring Boot and AWS Lambda

Microservices, complex but revolutionary way to refactor Java monoliths. Applications can scale and develop more quickly when they are divided into loosely connected, independently deployable services.

Why it Works:

• Domain-Driven Design (DDD) enables teams to align services with business capabilities.
• Decoupling increases maintainability and reduces interdependencies.
• Scalable Independently: Services scale individually based on demand, reducing waste.
  

AWS Tools:

• Spring Boot on AWS Lambda via AWS Serverless Java Container allows Java developers to leverage Lambda’s event-driven, pay-per-use model.
• Amazon API Gateway enables the exposure of microservices securely and at scale.
• Amazon Aurora or Amazon DynamoDB supports decoupled data storage.
  

Best Practice:

Use the Strangler Fig Pattern—gradually route functionality from the monolith to new microservices without rewriting the entire application at once.

Adopt Java Virtual Machine (JVM) Optimizations and Modern Runtimes

Modernizing Java applications involves more than architecture – it also includes optimizing runtime. Moving to current Java versions and using performance-optimized JVMs can have enormous improvements to startup time, memory usage, and throughput. 

Why it Works:

• Java continues to develop; employing newer Java versions (Java 17+, and going forward) provides performance enhancements and security features.

• GraalVM and Amazon Corretto provide enhanced JDK distributions that are tuned for cloud workloads. 

AWS Tools:

• Amazon Corretto is a free, production-ready distribution of OpenJDK (some instance configurations also support a 3-year Long Term Support). 

• AWS Lambda with SnapStart (for Java) has the potential to have ultra-fast startup times since the function is pre-initialized. 

• The Compute Optimizer recommends EC2 instance types based on your actual CPU and memory usage, thereby minimizing under-provisioning. 

Best Practice:

Adopt and test run CI/CD pipelines when you upgrade your Java versions to check runtime incompatibilities. Use the AWS CloudWatch Metrics to assess performance impacts from pre-production via the AWS CodeGuru Profiler.

Replatform to Managed Services: Let AWS Manage the Undifferentiated Heavy Lifting

Replatforming involves moving from self-managed middleware and databases to fully managed AWS services. This significantly reduces operational overhead and improves scalability and security posture.

Why it Works:

• Focus shifts from infrastructure management to value delivery.
• Managed services handle patching, high availability, backups, and scaling.
• Simplifies security compliance with built-in integrations (IAM, KMS, etc.).
  

AWS Tools:

• Replace self-managed Tomcat or JBoss servers with AWS Elastic Beanstalk or AWS App Runner for Java workloads.
• Move from on-premise databases to Amazon RDS (Relational Database Service) or Amazon Aurora.
• Integrate Amazon ElastiCache for caching layers and Amazon MQ for managed message brokers.
  

Best Practice:

Run performance benchmarks after migrating to managed services and tune configurations. Use AWS Well-Architected Tool to validate the architecture post-replatforming.

Automate with DevOps and Observability Pipelines

Modernization is incomplete without embracing automation and observability. Java applications benefit from mature CI/CD practices and real-time visibility into performance and reliability.

Why it Works:

• Automation drives innovation and removes the potential for human error
• Observability helps you catch anomalies and performance bottlenecks earlier in the lifecycle.

AWS Tools:

• Leverage AWS CodePipeline along with AWS CodeBuild, CodeDeploy, and CodeCommit to create end-to-end Java CI/CD pipelines.
• Use Amazon CloudWatch, AWS X-Ray, and Amazon DevOps Guru for metrics, logs, traces, and anomaly detection.
• Utilize AWS CDK (Cloud Development Kit) or Terraform to deliver Infrastructure as Code (IaC), so you can deploy in a consistent and version-controlled manner.

Best Practice:

Use a “shift-left” testing mindset by integrating unit, integration, and security tests further upstream in the pipeline. Leverage AWS services to perform blue/green or canary deployments for safer releases.

Conclusion: A Strategic Roadmap, Not a One-Time Project

AWS has been a popular tool for corporations for many years, and modernizing Java applications on AWS is not simply a technology challenge; it is a technology challenge with strategic alignment. First, organizations must do a full application portfolio assessment to know which workloads are potential candidates for replatforming, lift-and-shift, or full refactoring.

The five strategies; JVM optimization, containerization, microservices refactoring, replatforming, and automation all have different objectives; all five strategies combined offer a broader comprehensive roadmap for reducing technical debt, increasing scalability, and accelerating innovation.

Success will not come from the choice of one of these strategies over another, but from using any one, or combination of all five strategies as an incrementally and iteratively applied hybrid modernization approach based on developer expertise, business priorities, and funding flexibility. With the AWS ecosystem, organizations are not just modernizing Java applications; they are future-proofing their mission-critical systems for the next generation of digital transformation.