In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results.

Mono2Micro is an AI-based toolchain that provides recommendations for decomposing legacy web applications into microservice partitions. Mono2Micro consists of a set of tools that collect static and runtime information from a monolithic application and process the information using an AI-based technique to generate recommendations for partitioning the application classes. Each partition represents a candidate microservice or a grouping of classes with similar business functionalities. Mono2Micro takes a temporo-spatial clustering approach to compute meaningful and explainable partitions. It generates two types of partition recommendations. First, it computes business-logic-seams-based partitions that represent a desired encapsulation of business functionalities. However, such a recommendation may cut across data dependencies between classes, accommodating which could require significant application updates. To address this, Mono2Micro computes natural-seams-based partitions, which respect data dependencies. We describe the set of tools that comprise Mono2Micro and illustrate them using a well-known open-source JEE application.

John Backus introduced the Functional Programming (FP) system, the variable-free applicative system having reduction semantics. Backus has also introduced a unique expansion technique for reasoning about a class of recursive FP programs. As a natural outgrowth of this expansion technique, an FP program synthesis methodology is described in this paper. The methodology synthesizes recursive FP programs of the form f = p →, q ; E ( f . h ) from their preformulated case-by-case descriptions, which in turn come from given input-output example specifications. After explaining the methodology informally, formalization in the form of a definition and a synthesis theorem is introduced. A sufficient condition for the functional form E , for successful synthesis under the present methodology, is obtained structurally. Several illustrative examples of synthesis are also included.