Benchmarking Micro2Micro transformation: an approach with GNN and VAE

In the evolving landscape of software architecture, the shift from monolithic structures to agile, scalable microservices has revolutionized cloud-native application development. However, the inherent dynamism of microservices can lead to the inadvertent creation of unnecessary microservices, introd...

Full description

Saved in:
Bibliographic Details
Published in:Cluster computing 2024-07, Vol.27 (4), p.4171-4185
Main Authors: Chy, Md Showkat Hossain, Sooksatra, Korn, Yero, Jorge, Cerny, Tomas
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Automation
Clustering
Complexity
Composition effects
Computer Communication Networks
Computer Science
Efficiency
Landscape architecture
Machine learning
Modularity
Neural networks
Operating Systems
Optimization
Processor Architectures
R&D
Research & development
Software
Software engineering
Transformations (mathematics)
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the evolving landscape of software architecture, the shift from monolithic structures to agile, scalable microservices has revolutionized cloud-native application development. However, the inherent dynamism of microservices can lead to the inadvertent creation of unnecessary microservices, introducing complexity and inefficiency. Moreover, with a lack of control mechanisms in evolution, systems can lead to what is known as architecture degradation. This research ventures into the emerging domain of microservice-to-microservice transformation, a concept focused on optimizing existing cloud-native systems. We experiment with a machine learning methodology initially designed for monolith-to-microservices migration, adapting it to the complex microservices landscape, with a specific focus on the train-ticket application (Zhou in Association for Computing Machinery,  https://doi.org/10.1145/3183440.3194991 ), which is an established system benchmark in the community. To identify the optimal microservice distribution, we employ a combination of the Variational Autoencoder and fuzzy c-means clustering. Our results demonstrate a close resemblance to the original application in terms of structural modularity. Though they fall short of achieving the ideal interface number exhibited by the original microservices, our findings highlight the potential of automated microservice composition, effectively narrowing the gap between human-designed and machine-generated microservices and advancing the field of software architecture.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-04526-z