Microservice Decomposition via Static and Dynamic Analysis of the Monolith

Krause, Alexander; Zirkelbach, Christian; Hasselbring, Wilhelm; Lenga, Stephan; Kroger, Dan

doi:10.1109/icsa-c50368.2020.00011

Cited by 32 publications

(16 citation statements)

References 12 publications

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“…Serverless compute architecture [26] [32] was proposed for migrating monolithic applications with benefits. Suggested boundary context approach [31] that extends static and dynamic analysis for decompositions of microservices. Recommended algorithm approach [33] for extracting microservices migration.…”

Section: Literature Reviewmentioning

confidence: 99%

Legacy Digital Transformation: TCO and ROI Analysis

Mallidi

Sharma

Singh

2021

Int. j. electr. comput. eng. syst. (Online)

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Legacy Digital Transformation is modernizing or migrating systems from non-digital or older digital technology to newer digital technologies. Digitalization is essential for information reading, processing, transforming, and storing. Social media, Cloud, and analytics are the major technologies in today's digital world. Digitalization (business process) and Digital Transformation (the effect) are the core elements of newer global policies and processes. Recent COVID pandemic situation, Organizations are willing to digitalize their environment without losing business. Digital technologies help to improve their capabilities to transform processes that intern promote new business models. Applications cannot remain static and should modernize to meet the evolving business and technology needs. Business needs time to market, Agility, and reduce technical debt. Technology needs consist of APIs, better Security, Portability, Scalability, Cloud support, Deployment, Automation, and Integration. This paper elaborates different transformation/modernization approaches for Legacy systems written in very long or End of Life (EOL) systems to newer digital technologies to serve the business needs. EOL impacts application production, supportability, compliance, and security. Organizations spend money and resources on Digital Transformation for considering Investment versus Return on Investment, Agility of the System, and improved business processes. Migration and Modernization are critical for any Legacy Digital Transformation. Management takes decisions to proceed with Digital Transformation for considering Total Cost Ownership (TCO) and Return on Investment (ROI) of the program. The paper also includes a TCO-ROI calculator for Transformation from Legacy / Monolithic to new architectures like Microservices.

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Section: Literature Reviewmentioning

confidence: 99%

Legacy Digital Transformation: TCO and ROI Analysis

Mallidi

Sharma

Singh

2021

Int. j. electr. comput. eng. syst. (Online)

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“…In the same lines, User entity maintains a relationship with Item entity as the application might show the recommended items for purchase for that particular user based on the items purchased by the user in earlier transactions. [18], it can be visualized that the application module might be designed to have three microservices which can deal with the business functionality of each domain as shown in Fig. 2.…”

Section: ) Item Entity Responsibilitiesmentioning

confidence: 99%

Impacts of Decomposition Techniques on Performance and Latency of Microservices

Rudrabhatla¹

2020

IJACSA

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Micro service architecture (MSA) has undoubtedly become the most popular modern-day architecture, often used in conjunction with the rapidly advancing public cloud platforms to reap the best benefits of salability, elasticity and agility. Though MSA is highly advantageous and comes with a huge set of benefits, it has its own set of challenges. To achieve the separation of concerns and optimal performance, defining the boundaries for the services clearly and their underlying persistent stores is quintessential. But logically segregating the services is a major challenge faced while designing the MSA. Some of the guiding principles like Single responsibility principle (SRP) and common closure principle (CCP) are put in place to drive the design and separation of microservices. With the use of these techniques the service layer can be designed either by (i) Building the services related to a business subdomain and packaging them as a microservice; (ii) or Defining the entity relationship model and then building the services based on the business capabilities which are grouped together as a microservice; (iii) or understanding the big picture of the application scope and combining both the strategies to achieve the best of both worlds. This paper explains these decomposition approaches in detail by comparing them with the real-world use cases and explains which pattern is suitable under which circumstances and at the same time examines the impacts of these approaches on the performance and latency using a research project.

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“…Static and Dynamic Analysis of the Monolith(Krause et al, 2020). Conference paper P27 Towards Automated Microservices Extraction Using Multi-objective Evolutionary Search(Saidani et al, 2019).…”

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confidence: 99%

Defining and measuring microservice granularity—a literature overview

Vera-Rivera

Gaona

Astudillo

2021

PeerJ Computer Science

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Background Microservices are an architectural approach of growing use, and the optimal granularity of a microservice directly affects the application’s quality attributes and usage of computational resources. Determining microservice granularity is an open research topic. Methodology We conducted a systematic literature review to analyze literature that addresses the definition of microservice granularity. We searched in IEEE Xplore, ACM Digital Library and Scopus. The research questions were: Which approaches have been proposed to define microservice granularity and determine the microservices’ size? Which metrics are used to evaluate microservice granularity? Which quality attributes are addressed when researching microservice granularity? Results We found 326 papers and selected 29 after applying inclusion and exclusion criteria. The quality attributes most often addressed are runtime properties (e.g., scalability and performance), not development properties (e.g., maintainability). Most proposed metrics were about the product, both static (coupling, cohesion, complexity, source code) and runtime (performance, and usage of computational resources), and a few were about the development team and process. The most used techniques for defining microservices granularity were machine learning (clustering), semantic similarity, genetic programming, and domain engineering. Most papers were concerned with migration from monoliths to microservices; and a few addressed green-field development, but none address improvement of granularity in existing microservice-based systems. Conclusions Methodologically speaking, microservice granularity research is at a Wild West stage: no standard definition, no clear development—operation trade-offs, and scarce conceptual reuse (e.g., few methods seem applicable or replicable in projects other than their initial proposal). These gaps in granularity research offer clear options to investigate on continuous improvement of the development and operation of microservice-based systems.

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Microservice Decomposition via Static and Dynamic Analysis of the Monolith

Cited by 32 publications

References 12 publications

Legacy Digital Transformation: TCO and ROI Analysis

Legacy Digital Transformation: TCO and ROI Analysis

Impacts of Decomposition Techniques on Performance and Latency of Microservices

Defining and measuring microservice granularity—a literature overview

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