Discovering Microservices in Enterprise Systems Using a Business Object Containment Heuristic

Abstract

The growing impact of IoT and Blockchain platforms on business applications has increased interest in leveraging large enterprise systems as Cloudenabled microservices. However, large and monolithic enterprise systems are unsuitable for flexible integration with such platforms. This paper presents a technique to support the re-engineering of an enterprise system based on the fundamental mechanisms for structuring its architecture, i.e., business objects managed by software functions and their relationships which influence business object interactions via the functions. The technique relies on a heuristic for deriving business object exclusive containment relationships based on analysis of source code and system logs. Furthermore, the paper provides an analysis of distributing enterprise systems based on the business object containment relationships using the NSGA II software clustering and optimization technique. The heuristics and the software clustering and optimization techniques have been validated against two open-source enterprise systems: SugarCRM and ChurchCRM. The experiments demonstrate that the proposed approach can identify microservice designs which support multiple desired microservice characteristics, such as high cohesion, low coupling, high scalability, high availability, and processing efficiency.

A Roadmap for Migration System-Architecture Decision by Neutrosophic-ANP and Benchmark for Enterprise Resource Planning Systems

“…Wikipedia and IBM also use SOA [11], [13], [37][38][39]. Although many companies have invested in SOA aligned IT transformations, they did not harvest what it promised to provide [9], [20], [40], [41]. That emphasizes that -the migration decision must be making carefully and study well; otherwise the system causes a disaster‖.…”

“…Thereby, analysis techniques are one of drawbacks of architecture migration. Although these analysis effects on the software structure and the modernization processes, there is still a gap in the area of correlating structural and behavioral analysis [41]. Where, Critical success factors (CSF) are defined as ‗An area where an organization must perform well if it is to succeed'.…”

Availability and Scalability Optimized Microservice Discovery from Enterprise Systems

“…Apart from business object relationships, the number of execution calls between different microservices plays a major role in defining high performing microservices, because an excessive number of network calls can increase response times while decreasing the availability of the service [1]. Available research related to microservice discovery has considered the number of calls between different methods to suggest microservices for the developer by analysing system execution logs [14,15]. However, such research has not considered the possibility of evaluating scalability and availability to derive better microservice configurations from enterprise systems.…”

“…Apart from the above standard parameters, our algorithm requires further input, such as the BOs of the system (B), and execution graph nodes (N) and their relationships (R) extracted from the execution graphs. These details can be extracted from a software system based on the methodology described by De Alwis et al [14]. The population size (n) defines how many chromosomes are populated in a single generation, while the number of generations (Gen) defines the number of times the algorithm generates different populations before it stops.…”

“…For the running example, i.e., the chromosome '0, 1, 2, 3, 5, 4, 6, 7', this sum would be 4 + 3 + 3 = 10, because the costs of calls between the pairs of clusters in '0', '1, 2, 3, 5', '4, 6, 7', are '4', '3' and '3'. Further details for calculating the first two objectives for the NSGA II algorithm can be found elsewhere [14].…”