From Monolithic Systems to Microservices: A Decomposition Framework based on Process Mining

Taibi, Davide; Systä, Kari

doi:10.5220/0007755901530164

Cited by 66 publications

(59 citation statements)

References 20 publications

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“…Helps to understand how large the identified microservice is and to identify if any microservice is too big compared to others. The number of classes should be minimized because a smaller number of classes implies more independent development of the microservice ( Taibi & Syst, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…The number of duplicated classes helps one to reason about the different slicing options, considering not only the size of the microservices but also the number of duplications, that will be then reflected in the microservices’ development. Duplicated classes should be avoided since duplication adds to the system’s size and maintenance ( Taibi & Syst, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…(Continued ) Coupling between microservice (CBM). Taibi & Syst (2019) defined CBM extending the well-known coupling between objects; this coupling can occur through method calls, field accesses, inheritance, arguments, return types, and exceptions. They calculated the relative CBM for each microservice by dividing the number of external links between the number of classes in the microservices (Taibi & Syst, 2019).…”

Section: Coupling Metricsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Coupling Metricsmentioning

confidence: 99%

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Defining and measuring microservice granularity—a literature overview

Vera-Rivera

Gaona

Astudillo

2021

PeerJ Computer Science

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“…This system, per se, is also a microservice. Another remarkable contribution that microservices provide is the scalability and availability within any proposed architecture [26].…”

Section: Microservicesmentioning

confidence: 99%

A Container Orchestration Development that Optimizes the Etherpad Collaborative Editing Tool through a Novel Management System

et al. 2020

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The use of collaborative tools has notably increased recently. It is common to see distinct users that need to work simultaneously on shared documents. In most cases, large companies provide tools whose implementations have been a very complicated and expensive task. Likewise, their platform deployment requirements should be robust hardware infrastructures. It becomes even more critical when their main target is to reach scalability and highavailability. Therefore, this study aims to design and implement a microservices-based collaborative architecture using assembled containers in the cloud, enabling them to deploy Etherpad instances to guarantee high availability. To ensure such a task, we developed and optimized a central management system that creates Etherpad instances and continuously interacts with other Etherpad tools running on Docker containers. This design goes from the monolithic Etherpad instantiation and handling towards a service architecture, where every Etherpad is offered as a microservice. Furthermore, the management system follows (implements) the Observer, Factory Method, Proxy, and Service Layerpopular design patterns. This allows users to gain more privacy through access to validations and shared resources. Our results indicate both the correct operation in the automation of containers’ creation for new users who register in the system and quantifiable improvement in performance.

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“…The approaches presented in academic research broadly fall under static or dynamic techniques. Approaches based on static analysis (e.g., [2,3,5,10,11,14,16,17]) capture static call relations among class objects and, therefore, face challenges in dealing with dynamic features of Java Enterprise Edition (JEE) applications, such as reflection, dynamic class loading, and context and dependency injections. Dynamic techniques (e.g., [4,8,13]) capture runtime call relations and workload characteristics of an application; however the runtime traces do not capture alignment information about how classes and objects support different business functions which is a foremost concern in industry practices.…”

Section: Introductionmentioning

confidence: 99%

Mono2Micro: an AI-based toolchain for evolving monolithic enterprise applications to a microservice architecture

Kalia

Xiao

Lin

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Mono2Micro is an AI-based toolchain that provides recommendations for decomposing legacy web applications into microservice partitions. Mono2Micro consists of a set of tools that collect static and runtime information from a monolithic application and process the information using an AI-based technique to generate recommendations for partitioning the application classes. Each partition represents a candidate microservice or a grouping of classes with similar business functionalities. Mono2Micro takes a temporospatial clustering approach to compute meaningful and explainable partitions. It generates two types of partition recommendations. First, it computes business-logic-seams-based partitions that represent a desired encapsulation of business functionalities. However, such a recommendation may cut across data dependencies between classes, accommodating which could require significant application updates. To address this, Mono2Micro computes natural-seamsbased partitions, which respect data dependencies. We describe the set of tools that comprise Mono2Micro and illustrate them using a well-known open-source JEE application. CCS CONCEPTS • Software and its engineering → Software architectures; Software post-development issues.

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From Monolithic Systems to Microservices: A Decomposition Framework based on Process Mining

Cited by 66 publications

References 20 publications

Defining and measuring microservice granularity—a literature overview

Defining and measuring microservice granularity—a literature overview

A Container Orchestration Development that Optimizes the Etherpad Collaborative Editing Tool through a Novel Management System

Mono2Micro: an AI-based toolchain for evolving monolithic enterprise applications to a microservice architecture

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