A search-based identification of variable microservices for enterprise SaaS

Recently, SaaS applications are developed as a composition of microservices that serve diverse tenants having similar but different requirements, and hence, can be developed as variability-intensive microservices. Manual identification of these microservices is difficult, time-consuming, and costly, since, they have to satisfy a set of quality metrics for several SaaS architecture configurations at the same time. In this paper, we tackle the multi-objective optimization problem of identifying variable microservices aiming optimal granularity (new metric proposed), commonality, and data convergence, with a search-based approach employing the MOEA/D algorithm. We empirically and experimentally evaluated the proposed method following the Goal-Question-Metric approach. The results show that the method is promising in identifying fully consistent, highly reusable, variable microservices with an acceptable multi-tenancy degree. Moreover, the identified microservices, although not structurally very similar to those identified by the expert architects, provide design quality measures (granularity, etc.) close to (and even better than) the experts.