Legacy applications can be directly containerized to be presented as modernized applications for cloud environments. The other prominent option is to subdivide the legacy application into multiple components, where each component caters to a business functionality. Now every business functionality can be converted into a microservice. Thus, there are microservices-centric and container-centric methods for effecting legacy modernization and migration to cloud environments.
Any application can be directly containerized, but this transition may not work in the long run. Hence, applications have to be segmented into smaller, more easily-manageable pieces. Then those segments have to be service-enabled and then containerized. There are best practices, enabling patterns, knowledge guides, success stories, case studies, optimized processes, integrated and insightful platforms, and proven procedures for smoothening this sort of legacy remediation. Best-in-class frameworks and automated tools are flourishing to tackle the complexities that are associated with legacy transformations. Here's a list expressed by one modernization expert.
The first step is to break the monolithic application into a group of distinguishable components. These components can be easily and elegantly service-enabled and containerized. Those containerized images are stocked in publicly-available image repositories. These components have to be extremely modular (loosely or lightly coupled and highly cohesive) in order to contribute to the goals of modernization and migration. These are the business functionalities, the typical middle-tier components in any three-tiered application.
Having created a collection of microservices that together make up the business logic of the application, the second step is to build data access as a service. That is, develop data services and expose them so that any application can use them to get the right data to complete business tasks. This setup decouples business and data logic so that there's no possibility for any kind of dependency-initiated issues. This data-logic layer is the final one, as per the specifications of three-tier applications. In the first step, we focused on creating application containers. In the second step, we talked about volume containers in order to store data to empower applications accordingly.
The last step is all about testing. For an enterprise-scale application, we can have several microservices and their instances. Also, containers as the service runtime are now manifold. Microservices and their many instances can be run in separate containers and hence there will be many containers in a typical IT environment; that is, for an application, there can be multiple interactive and collaborative microservices. Each microservice can be run in multiple containers in order to support redundancy. Widely-demanded high availability can be achieved through multiple containers for a single microservice. Due to the fickle nature of containers, architects recommend many containers for hosting one microservice. To ensure high availability, there can be many instances of microservices. This way, if one service or container goes down, its service instances deployed in other containers come to the rescue. However, the real difficulty lies in testing such an intertwined environment. Precisely speaking, monolithic applications need to be tuned to become distributed and complicated applications. Though modern applications are agile, affordable, and adaptive, the management and operational complexities of microservices-centric applications are bound to escalate. Further on, detecting errors and debugging them to make applications error-free is a tedious job indeed. There are a few automated testing tools emerging for testing microservices. Experts are unearthing various ways of testing distributed microservices. Also, the testing procedure is being illustrated for composite microservices.