GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

Abstract

Microservices architecture is a new paradigm for developing a software system as a collection of independent services that communicate through lightweight protocols. In greenfield development, identifying the microservices is not a trivial task, as there is no legacy code lying around and no old development to start with. Thus, identification of microservices from requirements becomes an important decision during the analysis and design phase. Use cases play a vital role in the requirements analysis modeling phases in a model-driven software engineering process. It is a technique of capturing high-level user functions and scope of the system. In this paper, we propose GreenMicro, an automatic microservice identification technique that utilizes the use cases model and the database entities. Both features are the artifacts of analysis and design phase that depict complete functionality of an overall system. In essence, a collection of related use cases indicates a bounded context of the system that can be grouped in a suitable way as microservices. Therefore, our approach GreenMicro clusters close-knit use cases to recover meaningful microservices. We investigate and validate our approach on an in-house proprietary web application and three sample benchmark applications. We have mapped our approach to state-of-the-art software quality assessment attributes and have presented the results. Preliminary results are motivating and the proposed methodology works as anticipated in producing functionally cohesive and loosely coupled microservice candidate recommendations. Our approach enables the system architects to identify microservice candidates at an early analysis and design phase of development.

A Comprehensive Microservice Extraction Approach Integrating Business Functions and Database Entities

“…We used Java Call Graph utilities 4 for generating static call lists. "javacg-static" program reads classes from application's jar file, moves down to the method body, and prints a list of 'caller-callee relationships' for both classes and methods.…”

“…We apply five quality performance metrics namely-Structural Modularity (SM), Non-Extreme Distribution (NED), Inter-Partition Call percentage (ICP), Interface Number (IFN), and Business Context Purity (BCP) to measure the effectiveness of CMEA. A brief description for these metrics is mentioned in [4].…”

“…Generally, these services are developed using different technological stacks by separate teams [12]. Thus, many software business players like Amazon, Netflix, Uber, Spotify, and numerous small to medium-sized enterprises are opting for microservices-based solutions for their complex monolithic systems [4].…”

ACNS Conference on Cloud and Immersive Technologies in Education: Report

“…• Marc Baaden, CNRS, France [14] • Pablo Garcia Bringas, University of Deusto, Spain [89] • Oleksandr Burov, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine & University of Vienna, Austria [27] • Nadire Cavus, Near East University, North Cyprus [137] • El-Sayed El-Horbaty, Ain Shams University, Egypt [88] • Ramón Fabregat, University of Girona, Spain [35] • Irina Georgescu, Bucharest University of Economics, Romania [43] • Mustansar Ali Ghazanfar, University of East London, United Kingdom [58] • Anita Goel, University of Delhi, India [16] • Carina Gonzalez, Universidad de La Laguna, Spain [45] • Sven Hartmann, Clausthal University of Technology, Germany [150] • Michail Kalogiannakis, University of Crete, Greece [52] • Yuriy Kondratenko, Petro Mohyla Black Sea State University, Ukraine [69] • Francesco Lelli, Tilburg University, Netherlands [40] • Chung-Sheng Li, PwC, United States [32] • Piotr Lipiński, Technical University of Lodz, Poland [93] • Jinwei Liu, Florida A&M University, United States [80] • Alessandra Lumini, University of Bologna, Italy [101] • Rashid Mehmood, King Abdulaziz University, Saudi Arabia [90] • Vincenzo Moscato, University of Naples "Federico II", Italia [98] • Thomas Moser, St. Pölten University of Applied Sciences, Austria [26] • Ranesh Kumar Naha, University of Tasmania, Australia [100] • Stamatios Papadakis, University of Crete, Greece [79] • Michael M. Resch, HLRS, University of Stuttgart, Germany [76] • Nina Rizun, Gdańsk University of Technology, Poland [149] • Abdel-Badeeh M. Salem, Ain Shams University, Egypt [15] • Demetrios Sampson, University of Piraeus, Greece [111] • Antonio Sarasa Cabezuelo, Universidad Complutense de Madrid, Spain [29] • Prem Kumar Singh, Gandhi Institute of Technology and Management, India [132] • Daniel Thalmann, Swiss Federal Institute of Technology in Lausanne, Switzerland…”

Microservice Identification by Partitioning Monolithic Web Applications Based on Use-Cases

“…Under these circumstances, the modularization of web apps makes it easier to maintain increasingly complex web apps, and it can improve their performance [2]. Hence, various approaches, such as a method to partition web apps into layers [3] and a method to transform web apps into service-oriented architectures [4], have been proposed as methods for the modularization of web apps [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In addition, to achieve modularization, several companies (including Netflix, Amazon, and eBay) have recently migrated their monolithic web apps to microservice architectures [5].…”