About the Client
Formed in 1995, the client is an airport authority operating in the APAC region. The client leads the cargo airport space by serving more than 220 countries across the globe. With 71,500,000 passengers traveling annually, it is one of the busiest airports in the world.
The Business Challenge
The client had a critical legacy application based on WebSphere 9.0 on AIX 7.1 and Oracle. The client wanted to quickly migrate to AWS without having to rearchitect and convert the legacy application into microservices. At the same time, they also wanted to ensure the seamless scalability of this application, as it handled significant user load.
This being a live system, downtime was not an option. Another key challenge was that the application was in production for several years and had undergone significant tuning, customization, and static configuration. The client was concerned about disturbing a running critical system, with little formal documentation of the various configurations that have gone into the system over these years. This posed a significant business risk.
In the target, the client expected availability and scalability and also wanted to showcase that compared to the cost of hardware, support, maintenance & personnel, a cloud-based solution is cost-effective as well as future proof.
How CloudHedge Helped?
CloudHedge recommended to the client a three-stage approach which was a combination of their unique Discovery & Deployment (Cruize) Technology coupled with DevOps expertise.
Stage 1: Intelligent Discovery
The first stage in the process was to set up, deploy and configure the CloudHedge appliance (all prerequisites like Docker, images, etc. pre-bundled) on-premises in an air-gap environment which had strict regulations from the governing authority.
CloudHedge then leveraged its Discover solution which did an intelligent, in-depth discovery of the AIX servers to capture all the relevant application processes. This also equipped the client with the knowledge of application dependencies at OS level, port bindings, static configurations, etc. Having all this information upfront made the overall transformation project predictable and highlighted any risks, early in the project.
Stage 2: Dependency Resolution & Transformation
Based on the information gathered during discovery phase, CloudHedge professional services team was able to recommend the customer hotspots in the application configuration, which are required to be modified. These modifications prepare the application to run natively in a containerized environment. Apart from these hotspots and static configuration, the application dependencies like volume mounts, external services like LDAP JMS had to be evaluated to ensure that they would continue to be accessible from within the target environment.
Stage 3: Seamless Deployment to AWS
Once the application was containerized, CloudHedge used its proprietary application blueprint functionality to deploy a Kubernetes cluster onto AWS and use that as a target for our migrated application. The core benefit here was the quick ramp-up of client’s IT team, to embrace cloud and AWS platform achieved through Cruize. Cruize basically abstracted the complexities involved with first-time users of cloud technologies making overall adoption easier.
Use of CloudHedge Command Line Utility
To speed up the process, the team made use of CloudHedge Command Line Utility to integrate the deployment process into their existing CI/CD pipeline.
- CloudHedge discovered, containerized and deployed AIX based applications onto AWS, thereby achieving significant savings in terms of hardware, support & maintenance and personnel cost for the client.
- Since CloudHedge discovers the actual runtime, all the environment configuration applicable in existing production environment was captured and replicated as-is, thereby reducing risk.
- The process did not cause any disruption for users as the cutover to new system was seamless for users.
- Containers scale up new environments in minutes Vs days in the earlier process.
- The solution was deployed to AWS using automation, eliminating the need for highly skilled administrators and supervision further reducing operation costs by 60%.