The Applicability of System Migration Life Cycle (SMLC) Framework

AlThani, Bashair; Khaddaj, Souheil

doi:10.1109/dcabes.2017.38

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Instead of designing microservice for a new greenfield scenario, this work focuses explicitly on migrating monolith applications to microservices. This process involves three main phases: pre-migration, migration, and post-migration [67].…”

Section: Quality Metrics For Cloud-native Architecture (Rq2)mentioning

confidence: 99%

From Monolith to Microservice: Measuring Architecture Maintainability

Hasan¹,

Osman²,

Admodisastro³

et al. 2023

IJACSA

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The migration of monolithic applications to the cloud is a popular trend, with microservice architecture being a commonly targeted architectural pattern. The motivation behind this migration is often rooted in the challenges associated with maintaining legacy applications and the need to adapt to rapidly changing business requirements. To ensure that the migration to microservices is a sound decision for enhancing maintainability, designers must carefully consider the underlying factors driving this software architecture migration. This study proposes a set of software architecture metrics for evaluating the maintainability of microservice architectural designs for monolith to microservice architecture migration. These metrics consider various factors, such as coupling, complexity, cohesion, and size, which are crucial for ensuring that the software architecture remains maintainable in the long term. Drawing upon previous product quality models that share similar design properties with microservice, we have derived maintainability metrics that can help measure the quality of microservice architecture. In this work, we introduced our first version of structural metrics for measuring the maintainability quality of microservice architecture concerning its cloud-native characteristics. This work allows us to get early feedback on proposed metrics before a detailed evaluation. With these metrics, designers can measure their microservice architecture quality to fully leverage the benefits of the cloud environment, thus ensuring that the migration to microservice is a beneficial decision for enhancing the maintainability of their software architecture applications.

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Section: Quality Metrics For Cloud-native Architecture (Rq2)mentioning

confidence: 99%

From Monolith to Microservice: Measuring Architecture Maintainability

Hasan¹,

Osman²,

Admodisastro³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Decomposing monolith applications into autonomous subsystems is a ubiquitous strategy in modernizing legacy applications (Taibi & Systä, 2019). Althani and Khaddaj (Bashair Althani & Khaddaj, 2017) proposed integrating the quality aspects into the migration process by considering the impact on migration cost and risk. However, the authors did not facilitate the identi cation approach to establish the best decomposition outputs, thus evaluating its quality.…”

Section: Related Workmentioning

confidence: 99%

A Quality Driven Framework for Decomposing Legacy Monolith Applications to Microservice Architecture

Hasan

Osman

Admodisastro

et al. 2023

Preprint

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Transforming monolith applications to microservice architecture is a common cloud migration strategy for businesses to accomplish cloud-native benefits. However, decomposing monolith applications is a challenging task that requires experience, skills, and dedication to initiate this process, and often, the migrated product quality is neglected. The lack of relevant guidelines on the design quality for distributed cloud environment architecture such as microservice further exacerbates this concern. We propose a quality-driven decomposition framework for migrating monolith applications to the cloud-native architecture. Our approach implies six activities in decomposing monolith applications from the source code to the microservice architecture. This framework supports various architectural design properties related to maintainability quality. Furthermore, this framework enhances the machine learning approach to enable automatic microservice identification, hence evaluating the design quality using a scoring-based approach. We use five applications to evaluate our approach, and the results show that our framework can provide insightful judgment to the designer regarding microservice design quality.

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