Microservice Remodularisation of Monolithic Enterprise Systems for Embedding in Industrial IoT Networks

This paper addresses the challenge of decoupling “back-office” enterprise system functions in order to integrate them with the Industrial Internet-of-Things (IIoT). IIoT is a widely anticipated strategy, combining IoT technologies managing physical object movements, interactions and contexts, with business contexts. However, enterprise systems, supporting these contexts, are notoriously large and monolithic, and coordinate centralised business processes through software components dedicated to managing business objects (BOs). Such objects and their associated operations are difficult to manually decouple because of the asynchronous and user-driven nature of the business processes and complex BO dependencies, such as many-to-many and aggregation relationships. Here we present a software remodularisation technique for enterprise systems, to support the discovery of fine-grained microservices, which can be extracted and embedded to run on IIoT network nodes. It combines the semantic knowledge of enterprise systems, i.e., the BO structure, with syntactic knowledge of the code, i.e., various dependencies at the level of classes and methods. Using extracted feature sets based on both semantic and syntactic dependencies, K-Means clustering and optimisation is then used to recommend microservices, i.e., redistributions of BO operations through microservices from BO-centric components of enterprise systems. The approach is validated using the Dolibarr open source ERP system, in which we identify processes comprising both “edge” operations and request-response calls to the Cloud-based enterprise system. Through experimentation using Amazon GreenGrass deployments, simulating IIoT nodes, we show that the recommended microservices demonstrate key non-functional characteristics, of high execution efficiency, scalability and availability.