Service Granularity in Industrial Automation and Control Systems

Homay, Aydin; Sousa, Mário de; Zoitl, Alois; Wollschlaeger, Martin

doi:10.1109/etfa46521.2020.9212048

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Services and procedures together with their parameters and variables compose a service specification that is the most important component of every PEA since it defines the module's functionality. The process of designing the service specification is a complex engineering process that connects process and equipment knowledge with the requirements from the side of the end user [10,11].…”

Section: Modular Automationmentioning

confidence: 99%

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

Neuendorf

Khaydarov

Schlander

et al. 2023

Chemie Ingenieur Technik

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Image analysis presents a set of powerful methods to receive additional information about multiphase processes. It enables the development of advanced applications for process monitoring and optimization or, so‐called, soft sensors. However, the integration of advanced smart sensor systems based on image analysis into the process control system presents a complex task. To address this challenge, a modular automation concept offers a standardized interface to integrate modules. This paper presents an integration profile as a service specification that allows a plug‐and‐measure integration of smart visual sensors into modular plants. To verify the concept, we applied it to three different use cases. At the end, we discuss open challenges in the integration of complex analysis systems with multidimensional data streams into modular plants.

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Section: Modular Automationmentioning

confidence: 99%

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

Neuendorf

Khaydarov

Schlander

et al. 2023

Chemie Ingenieur Technik

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“…According to Hassan et al [8] and Homay et al [10] microservice granularity definition (see introduction), a relationship between the microservice granularity with coupling, modularity and complexity of the system is evident. Changing the size and scope of microservices implies changes in the coupling, modularity, and complexity of the system.…”

Section: A Formal Specification Of the Granularity Modelmentioning

confidence: 99%

“…Granularity adaptation entails merging or decomposing microservices thereby moving to a finer or more coarse-grained granularity level. Homay et al [10] stated that "the problem in finding service granularity is to identify a correct boundary (size) for each service in the system. In other words, each service in the system needs to have a concrete purpose, as decoupled as possible, and add value to the system.…”

Section: Introductionmentioning

confidence: 99%

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

et al. 2021

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The microservice granularity directly affects the quality attributes and usage of computational resources of the system, determining optimal microservice granularity is an open research topic. Microservices granularity is defined by the number of operations exposed by the microservice, the number of microservices that compose the whole application, and its complexity and dependencies. This paper describes "Microservice Backlog (MB)", a semiautomatic model for defining and evaluating the granularity of microservice-based applications; MB uses genetic programming technique to calculate at design time the granularity of each microservice from the user stories in the "product backlog" or release planning; the genetic algorithm combined coupling, cohesion, granularity, semantic similarity, and complexity metrics to define the number of microservices, and the user stories associated with each microservice. MB decomposes the candidate microservices, allowing to analyze graphically the size of each microservice, as well as its complexity, dependencies, coupling, cohesion metrics, and the number of calls or requests between microservices. The resulting decomposition (number of microservices and their granularity) performed by MB shows less coupling, higher cohesion, less complexity, fewer user stories associated with each microservice, and fewer calls among microservices. MB was validated against three existing methods, using two state-of-the-art applications (Cargo Tracking and JPet-Store), and one real-life applications (Foristom Conferences). The development team and/or architect can use metrics to identify the critical points of the system and determine at design time how the microservice-based application will be implemented.

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“…Granularity adaptation entails merging or decomposing microservices thereby moving to a finer or more coarse-grained granularity level ( Hassan, Bahsoon & Kazman, 2020 ). Homay et al (2020) stated that “the problem in finding service granularity is to identify a correct boundary (size) for each service in the system. In other words, each service in the system needs to have a concrete purpose, as decoupled as possible, and add value to the system.…”

Section: Introductionmentioning

confidence: 99%

“…A service has a correct or good granularity if it maximizes system modularity while minimizing the complexity. Modularity in the sense of flexibility, scalability, maintainability, and traceability, whereas complexity in terms of dependency, communication, and data processing” ( Homay et al, 2020 ).…”

Section: Introductionmentioning

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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Service Granularity in Industrial Automation and Control Systems

Cited by 9 publications

References 11 publications

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

Defining and measuring microservice granularity—a literature overview

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