A multi-model based microservices identification approach

Abstract

Microservices are hailed for their capabilities to tackle the challenge of breaking monolithic business systems down into small, cohesive, and loosely-coupled services. Indeed, these systems are neither easy to maintain nor to replace undermining organizations' efforts to cope with user's changing needs and governments' complex regulations. Microservices constitute an architectural style for developing a new generation of systems as a suite of services that, although they are separate, engage in collaborative execution and communication sessions. However, microservices success depends, among many other things, on the existence of an approach that would automatically identify the necessary microservices according to organizations' requirements. In this paper, we present such an approach and demonstrate its technical doability in the context of a case study, Bicing, for renting bikes. Some salient features of this approach are business processes as input for the identification needs, three models known as control, data, and semantic to capture dependencies between these processes' activities, and, finally, a collaborative clustering technique that recommends potential microservices. Conducted experiments in the context of Bicing clearly indicate that our approach outperforms similar ones for microservices identification and reinforce the important role of business processes in this identification. The approach constitutes a major milestone towards a better architectural style for future microservices systems.

GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

“…In [15], an automatic identification approach has been proposed from a set of business processes. Their multimodel approach combines different independent models that represent a business process like control, data, and semantic dependencies [16]. Rivera et al [17] propose another intelligent and optimal method that works on user stories to decompose the functionalities or requirements of the application into microservices.…”

“…The above listed baseline approaches do not perform their analysis on the Cargo Tracking System. So, for this application, we compare our approach with other four wellknown baselines for microservice identification: Service Cutter [39], API Interface Analysis [12], DFD Analysis [9] and Business Processes Analysis [16]. Service Cutter 10 is a state-of-the-art approach for microservice identification.…”

“…Non-Extreme Distribution (NED) [28], [36] as used in other baseline techniques [9], [12], [16], [39]. In general, all the metrics are based on coupling, cohesion and number of interactions between microservices.…”

A Distributed Adaptive Control Model for Microgrids with a Microservice-oriented Architecture

“…Single services in microservices architectures are usually dynamically scalable and can be split and merged into multiple microservices according to actual needs as the microgrid evolves [9][10]. The application of microservices architecture to microgrids is essentially to reduce the services to pieces and to achieve the purpose of each of them [11][12]. The relatively small span of services in the microgrid leads to a centralized component that has a greater impact on the individual branch architectures and requires the integration of the various microservices during the application.…”

Building a Unified Spatio-Temporal Data Model for Grid Resources Based on Microservice Architecture

“…As the current development needs of the grid become more intelligent and visualized, the requirements for data and model accuracy are relatively higher. The unified Spatio-temporal data model for grid resources is, in essence, the need to integrate individual independent regional grids, under the influence of microservices architecture [14][15] . On the basis of the above description, the steps to design a unified resource scheduling scheme based on a microservice architecture are completed.…”