By: Laura Mitchell, Chief Technology Officer – Tech Innovations
By: Kevin Sanders, Principal Partner Solutions Architect – AWS
For over sixty years, COBOL has served as the backbone for critical systems in banking, insurance, and government sectors. Despite its reliability, approximately 85% of COBOL applications operate on outdated mainframe systems that are costly, hard to scale, and disconnected from modern cloud technologies. Organizations now confront a crucial question: how can we modernize these vital applications without rewriting millions of lines of code?
Amazon Elastic Kubernetes Service (Amazon EKS) presents a solution. By containerizing COBOL applications and deploying them on EKS, companies can retain their existing code while leveraging the flexibility, scalability, and efficiency of modern cloud infrastructure. When integrated with large language models (LLMs), businesses can further unlock the potential of their COBOL data, introducing automation, real-time analytics, and actionable insights. For a deeper dive into similar topics, check out this blog post.
Harnessing AI to Drive Business Value from COBOL
COBOL systems excel at generating structured, rules-based data, but extracting insights from that data has not been their strong suit. This is where LLMs come into play. Picture a COBOL-based financial system generating thousands of transaction logs each day. Analysts would traditionally sift through these reports manually. However, with LLM integration, those logs can be automatically summarized, highlighting anomalies and even flagging potential fraudulent activity. In the insurance industry, COBOL systems generate raw claim data that agents turn into formal reports. LLMs can streamline this process, drastically reducing turnaround time and enhancing accuracy. Even in customer service, where COBOL systems underpin backend operations, LLMs can facilitate real-time, chatbot-driven interactions utilizing legacy data.
Re-platforming COBOL with Amazon EKS
Amazon EKS provides a fully managed Kubernetes environment tailored for running containerized COBOL workloads. Unlike traditional mainframe setups that demand substantial upfront investments and inflexible scaling, EKS offers an adaptable infrastructure that dynamically adjusts to workload demands. For batch processes typical of COBOL systems, Kubernetes CronJobs simplify scheduling, enabling enterprise-level automation. Incorporating services like Amazon EFS for persistent file storage ensures COBOL workloads can read and write data without necessitating code alterations. Integration with Amazon RDS allows COBOL data to seamlessly engage with relational cloud services.
Given that COBOL workloads often handle sensitive financial and governmental information, security and compliance are critical. EKS addresses these concerns with fine-grained access controls through AWS IAM, encryption at rest via AWS Key Management Service, and private networking through AWS PrivateLink. Multi-AZ deployment ensures workloads are distributed across availability zones for enhanced availability, thereby mitigating downtime risks.
Observability is also significantly improved. Tools like Amazon CloudWatch, AWS X-Ray, and Amazon Managed Services for Prometheus deliver detailed metrics and traceability, enabling teams to monitor legacy workloads’ performance in the cloud.
Enhancing COBOL Workflows with Generative AI
Once COBOL workloads are containerized and operational on EKS, they become candidates for advanced enhancements. Typically, COBOL batch jobs produce structured outputs—like CSV files or fixed-width reports—stored on a persistent file system like Amazon EFS. From there, a containerized microservice can retrieve this output, format it into JSON or prompt-based formats, and send it to an LLM for analysis.
These LLMs do not replace the COBOL logic; rather, they complement it. The processed insights can be archived in Amazon S3 or a relational database, displayed on dashboards, or provided via APIs to downstream business applications. This architecture allows legacy systems to create modern business value without the burden of complete rewrites. For expert insights on this topic, refer to this resource.
A Case Study in Modernization
A prominent automotive supply company depended on legacy COBOL applications to support its essential business functions. These applications resided on outdated, on-premises infrastructure that became increasingly costly and unreliable. The company faced performance bottlenecks multiple times daily, leading to rising maintenance expenses for aging hardware and significant risks.
The organization opted for modernization through Amazon EKS in collaboration with Tech Innovations. Reliability soared with high availability across multiple zones. Following the decommissioning of legacy hardware and migration to elastic cloud infrastructure, costs were cut by 18%. By integrating LLMs to analyze COBOL-generated reports, the company gained quicker insights into operations, enhancing inventory management and accelerating order processing.
Implementation Overview
The Amazon EKS cluster was established with two node groups to support COBOL workloads and LLM services. Standard compute instances catered to COBOL processing, while a GPU-enabled node group facilitated the running of self-hosted LLM models. Both node groups utilized encrypted Amazon EBS volumes for storage, with IAM roles ensuring secure access to AWS services.
Amazon EFS provided persistent storage for COBOL data, configured with mount targets across availability zones using Terraform. The COBOL code remained unchanged—we utilized GnuCOBOL, an open-source COBOL compiler, allowing us to run the source code directly in a Linux-based container. Containerization employed Docker, eliminating the need for emulation. This approach minimized migration complexity, enabling us to deploy COBOL applications on Amazon EKS alongside contemporary LLM services. Each application was deployed via Helm charts and managed with ArgoCD, ensuring consistent and scalable delivery across environments.
Supporting services included ExternalDNS for managing DNS records, the AWS Load Balancer Controller for traffic ingress, and the Nvidia device plugin to facilitate GPU access within pods.
Best Practices for Long-Term Success
Preprocessing is critical when merging AI with COBOL workflows. Legacy output must be transformed into formats suitable for LLMs, typically JSON or structured prompts. A modular design keeps COBOL processing and AI enhancements as separate services, enabling independent scaling and debugging.
Security should always be a priority. Utilize IAM roles to restrict access, encrypt data both at rest and in transit, and refrain from exposing internal workloads to the public internet. Monitoring is equally vital. Cloud-native observability tools provide visibility into processing pipelines, allowing teams to track COBOL execution and AI processing performance in real time.
Looking Ahead
Modernizing COBOL with Amazon EKS transcends mere cost savings; it is a pathway to future-proofing decades-old systems. By merging COBOL’s reliability with Kubernetes’ scalability and the intelligence of LLMs, enterprises can harness the strengths of their legacy code while driving innovation. For additional insights on onboarding processes, this resource is an excellent read.
Leave a Reply