Microservices

Shadija, Dharmendra; Rezai, Mo; Hill, Richard

doi:10.1145/3147234.3148093

Cited by 29 publications

(6 citation statements)

References 13 publications

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“…Granularity: The functional scope or size of a microservice is generally referred to as the microservice granularity (Shadija et al 2017). Granularity can be mathematically represented by the ratio between the original system size and the total number of microservices in the decomposition (Driessen Famkeand Ferreira Pires 2024).…”

Section: Software Quality Properties For Maintainabilitymentioning

confidence: 99%

A Deep Learning-based Approach for Predictive Evaluation of Microservice Maintainability

YILMAZ,

KÖSE,

BUZLUCA

2024

Preprint

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Microservice Architecture (MSA) has emerged as a prominent paradigm in software system design, emphasizing decomposing monolithic applications into independent and modular functional services. This architectural approach provides a number of benefits; however, realizing these benefits requires a robust evaluation strategy focused on assessing the quality of the software system. This study proposes an innovative learning-based approach to evaluate the microservices’ quality, particularly maintainability. It is based on a deep learning technique that predicts the maintainability levels of microservices into three categories: low, medium, and high, with the low category indicating the need for refactoring. The prediction technique asses maintainability by feeding source-code metric values from different open-source microservice projects as inputs and obtaining results directly through transfer learning. The proposed method employed transfer learning and achieved % a 91.83 F1 score on the validated dataset obtained from open-source projects for predicting the need for refactoring services. Additionally, it reached %82 overall accuracy in the three class categorizations, showing notable performance. Considering these findings, it can be stated that the proposed learning-based evaluation is an effective method to assess microservice quality. As a result, this proposed method offers valuable insights for informed decision-making in software development and maintenance processes.

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Section: Software Quality Properties For Maintainabilitymentioning

confidence: 99%

A Deep Learning-based Approach for Predictive Evaluation of Microservice Maintainability

YILMAZ,

KÖSE,

BUZLUCA

2024

Preprint

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“…A microservice implements and supports a dedicated business capability which is normally comprised of several functionalities coupled in a service [68,202]. How big or small microservices should be is an ongoing debate [245,265]. However, when services are designed to only provide a single functionality -as is the case with FaaS -then the size should be even smaller.…”

Section: Related Technologiesmentioning

confidence: 99%

A Simulation Framework for Function as a Service

Manner

2024

Schriften Aus Der Fakultät Wirtschaftsinformatik Und Angewandte Informatik Der Otto-Friedrich-Universität Bamberg

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Serverless Computing is seen as a game changer in operating large-scale applications. While practitioners and researches often use this term, the concept they actually want to refer to is Function as a Service (FaaS). In this new service model, a user deploys only single functions to cloud platforms where the cloud provider deals with all operational concerns – this creates the notion of server-less computing for the user. Nonetheless, a few configurations for the cloud function are necessary for most commercial FaaS platforms as they influence the resource assignments like CPU time and memory. Due to these options, there is still an abstracted perception of servers for the FaaS user. The resource assignment and the different strategies to scale resources for public cloud offerings and on-premise hosted open-source platforms determine the runtime characteristics of cloud functions and are in the focus of this work. Compared to cloud offerings like Platform as a Service, two out of the five cloud computing characteristics improved. These two are rapid elasticity and measured service. FaaS is the first computational cloud model to scale functions only on demand. Due to an independent scaling and a strong isolation via virtualized environments, functions can be considered independent of other cloud functions. Therefore, noisy neighbor problems do not occur. The second characteristic, measured service, targets billing. FaaS platforms measure execution time on a millisecond basis and bill users accordingly based on the function configuration. This leads to new performance and cost trade-offs. Therefore, this thesis proposes a simulation approach to investigate this tradeoff in an early development phase. The alternative would be to deploy functions with varying configurations, analyze the execution data from several FaaS platforms and adjust the configuration. However, this alternative is time-consuming, tedious and costly. To provide a proper simulation, the development and production environment should be as similar as possible. This similarity is also known as dev-prod parity. Based on a new methodology to compare different virtualized environments, users of our simulation framework are able to execute functions on their machines and investigate the runtime characteristics for different function configurations at several cloud platforms without running their functions on the cloud platform at all. A visualization of the local simulations guide the user to choose an appropriate function configuration to resolve the mentioned trade-off dependent on their requirements.

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“…[18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. [36], [37], [38], [39], [40], [41], [42] Performance. Scalability.…”

Section: Related Workmentioning

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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Microservices

Cited by 29 publications

References 13 publications

A Deep Learning-based Approach for Predictive Evaluation of Microservice Maintainability

A Deep Learning-based Approach for Predictive Evaluation of Microservice Maintainability

A Simulation Framework for Function as a Service

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

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