Microservices Research Articles

CI/CD model for optimizing software deployment in SMEs

Small and Medium-sized Enterprises (SMEs) face unique challenges in software deployment, often dealing with limited resources, budget constraints, and the need for fast, reliable updates. This review outlines a Continuous Integration and Continuous Deployment (CI/CD) model specifically tailored to optimize software deployment in SME environments. The proposed CI/CD framework enhances operational efficiency by automating the integration, testing, and deployment processes, significantly reducing the risk of human error and deployment failures. The model leverages key CI/CD practices, including automated testing, version control, and continuous monitoring, to ensure seamless updates and minimal downtime. By integrating tools like Jenkins, GitLab, and Docker, SMEs can streamline code integration, enhance collaboration between development and operations teams, and enable faster, more frequent releases. Automated testing ensures that code changes are validated before deployment, identifying and addressing potential issues early in the development cycle. The framework also emphasizes rollback capabilities and monitoring systems to further reduce deployment risks. Rollbacks allow for quick recovery in case of failure, while continuous monitoring enables real-time detection of performance issues, helping SMEs maintain operational uptime and minimize service disruptions. Moreover, the model supports a microservices architecture, allowing SMEs to scale their applications efficiently without compromising stability. Incorporating CI/CD practices fosters agility, enabling SMEs to respond rapidly to market demands and customer feedback. This model not only improves software quality and deployment speed but also aligns with the budgetary and operational limitations of SMEs. It provides a cost-effective solution that enhances overall system reliability and reduces the frequency of software-related incidents. This CI/CD model serves as a practical approach to software deployment, empowering SMEs to optimize their software development lifecycle while ensuring continuous improvement and operational stability.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Software architecture for implementing a personal budget manager with artificial intelligence

The rapid adoption of artificial intelligence (AI) in consumer markets has led to the need for extensive research into its impact on consumer behavior, particularly in how it exploits cognitive biases. This raises ethical questions and highlights the need to balance such influences with AI systems that help consumers, not manipulate them. The development and implementation of an intelligent financial management system, which not only automates financial processes, but also integrates AI-driven tools to support better financial decision-making, contributes to solving these problems. The work contains a description of the development of the architecture of an intelligent financial management system, which is aimed at solving problems related to financial illiteracy, the complexity of financial markets, and the impact of cognitive biases on financial decision-making. The system provides for the use of modern technologies of artificial intelligence and machine learning to automate financial processes and provide users with personalized recommendations. The development of a system based on microservice architecture requires the use of certain architectural patterns that ensure efficiency, reliability and scalability, so the work was based on a critical analysis of modern architectural paradigms, such as microservice architecture and Event-Driven Architecture. The implementation of AI supports ethical guidelines that prioritize transparency, fairness and consumer autonomy, i.e. ensure that the AI ​​system is free from bias, actions can be easily interpreted by users. The methodology of this research is based on an interdisciplinary approach that combines the analysis of architectural solutions, methods of machine learning and artificial intelligence with methods of ensuring the reliability and scalability of software systems.

Utilizing Machine Learning for Intelligent Data Management in Event-Driven Microservices Architectures

The following research paper is a case study of three specific machine learning algorithms, LSTM networks, GBMs and RL for managing data in event-driven microservices architectures. This work assesses the performance of these algorithms using factors including anomaly detection, resource consumption prediction, and service coordination concerning such challenges as anomalies. LSTM networks was used in assessment of the anomalous patterns with accuracy reaching 92%, and false positive rate of 5%. The ability of the GBMs was evaluated for its capacity to accurately predict resource requirements, and in turn, minimize resource over-commitment and under-commitment that occurred, achieving 89% accuracy, with a percentage variation of 18% and 14% accordingly. The RL algorithms proved their potential to enhance the decision-making process governing the orchestration of services and failure recovery with the decision-makers achieving 22% increase in decision making accuracy and failure recovery time was reduced to 4. 5 minutes. These algorithms are discussed separately in the next sections with reference to their applications in intelligent data management in business event processing system. These findings are useful to improve the application of these machine learning techniques to increase the performance, utilization of resources and reliability of the system.

Design and implementation of a scalable IoT-based real-time environmental monitoring and alarm system: an experimental study

Digital transformation is crucial for organizations to survive, be competitive, and grow in the modern age. IoT is the key to enabling this transformation by connecting devices and optimizing processes. This paper presents the design and implementation of a generic IoT-based real-time environmental monitoring and alarm system. The platform is validated by applying it to a manufacturing plant scenario, where various sensors simulate industrial conditions. Scalable message distribution systems such as MQTT and Apache Kafka facilitate reliable data transmission. A microservice architecture is constructed for the backend services to ensure uninterrupted and high throughput services in the application domain. Instead of deploying a real WSN, traffic generation services were chosen to minimize costs, provide greater control and flexibility, and facilitate faster, scalable testing in a controlled environment. The platform also features an integrated alarm system with an event definition module, which allows users to define custom action rules. This flexible, scalable, and resilient architecture can be used across a wide range of application domains that require digital transformation. The experimental study demonstrates the platform's capabilities and great potential for broader IoT applications.

Incorporation of computational routines in a microservice architecture in AgDataBox platform

Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.

Open source container orchestration for Industry 4.0 – requirements and systematic feature analysis

Container-based virtualization is a popular technique, e.g., to realize microservice architectures. Recently, containers became popular in Industry 4.0 / IIoT systems, which typically consist of hundreds of (edge) devices and machines. In such setups, efficient management of containers is essential as offered by container orchestrators like Kubernetes. However, currently no specific overviews discussing orchestrator capabilities for Industry 4.0 are available. In this paper, we analyze nine open source container orchestrators for their application in Industry 4.0 or IIoT settings as a basis for future research and development. We contribute a systematic literature review to identify 23 basic orchestration requirements. We complement this by insights from an intensive requirements collection in a research project on intelligent industrial production, as well as selected features from a published generic orchestrator analysis. From these 66 requirements, we derive a requirements/feature taxonomy, which we use to analyze the nine open source orchestrators including Kubernetes. We show that there is, e.g., still a lack of support regarding edge devices, IIoT protocols, security mechanisms, and specialized resources for artificial intelligence.

Microservices architecture to enable an open platform for realizing zero defects in cyber-physical manufacturing

Abstract The market’s demand for high-quality products necessitates innovative manufacturing approaches that emphasize flexibility, adaptability, and the reduction of defects. Traditional systems are currently evolving towards embracing I4.0 technologies, including data collection, processing, analytics and digital twin, aiming for zero-defect manufacturing quality. This paper introduces an open platform, compliant with RAMI4.0 standards, designed to improve manufacturing quality. The platform integrates data using Asset Administration Shells with microservices adaptation for data ingestion and advanced analytics. Additionally, it incorporates Non-Destructive Inspection tools, demonstrating a seamless integration of measurement and quality assurance. This study details the architectural specification and validation of the openZDM platform, employing microservices to ensure flexibility, modularity and scalability, aligning with the RAMI4.0 model. The platform was validated through deployment in an automotive assembly line, highlighting its effectiveness in integrating inspection scenarios and early defect detection tools. The architecture employs a choreographed approach to manage loosely coupled microservices, enabling efficient data lifecycle management from collection through analytics to visualization.

Servitization and product service system: A literature review on value creation

In recent years, the concept of servitization has been widely applied in the management of enterprises. However, changing customer demands and competitive pressures of the market have compelled enterprises to seek innovation. Simultaneously, in the context of the Industry 4.0 trend, the rapid development of digital technologies has led to an increasing number of studies on the integration of servitization and digitization. Therefore, this study, based on VOSviewer, Gephi, Scimago Graphica, and Histcite, analyzes the current state and development trends of research on servitization and product service system innovation from the perspective of value creation. Initially, basic statistical characteristics such as the influential author, geographical cooperation, and highly cited papers are presented by collecting relevant data from the Web of Science core collection database. Subsequently, this study explores the research hotspots and evolutionary trends in servitization and product service system innovation through keyword co-occurrence analysis and cluster analysis. Lastly, from a literature analysis perspective, the application of advanced technologies in servitization, digital servitization from an ecosystem perspective, and the innovative use of microservices architecture in product service systems are summarized and forecasted as research directions, providing theoretical guidance for future research in servitization.

A Brief Review of the Automation of Dependency Satisfaction With in Microservices Architectures

A Brief Review of the Automation of Dependency Satisfaction With in Microservices Architectures

Research on monitoring and energy management systems for energy storage stations on the power generation side

Abstract Addressing the challenges of limited operational efficiency, inadequate battery safety, inflexible system scalability, inadequate AGC/AVC functionality, and low levels of intelligent operation and maintenance encountered by energy storage power stations integrated with new energy generation sources, this article delves into the pivotal role of these power stations on the generation side. It meticulously examines the network architecture, functional framework, and pivotal technologies underpinning the system. Furthermore, the article proposes a comprehensive monitoring and energy management system tailored specifically for energy storage on the generation side. The system uses micro-service architecture and container technology to realize the processing and storage of massive data. Through complete isolation of the information network and control network and millisecond communication with GOOSE, the charging and discharging response speed of the energy storage station is greatly improved. The power allocation algorithm based on device status recognition completes the system’s high-precision power control of PCS, and this system uses big data analysis and data mining technology to conduct a comprehensive statistical analysis of battery health status, power station operation efficiency, and equipment operation status and energy consumption. The design of the system has favorable technical and application guidance for the subsequent energy management design and engineering application of the energy storage power station.

Scalable Microservices Architecture: Leadership Approaches for High-Performance Retail Systems

The rapid evolution of retail systems in the digital age necessitates the adoption of scalable microservices architectures to meet the demands of high-performance and responsive platforms. As the retail sector increasingly relies on technology to drive customer engagement, operational efficiency, and competitive advantage, the role of leadership in designing and implementing scalable microservices architectures becomes critical. This paper explores leadership approaches that are essential for guiding the successful deployment of microservices in high-performance retail systems. The shift from monolithic to microservices architectures represents a fundamental change in how retail systems are built and maintained. Microservices, characterized by their modularity, independence, and scalability, offer significant advantages over traditional monolithic architectures, particularly in handling the dynamic demands of modern retail environments. However, the successful implementation of microservices requires more than just technical expertise; it demands strategic leadership that understands the complexities of system design, cross-functional collaboration, and long-term maintenance.

Leadership in Platform Engineering: Best Practices for High-Traffic E-Commerce Retail Applications

High-traffic retail applications need great platform engineering leadership to assure operational excellence and scalability in the continually changing e-commerce industry. This study examines how leadership matters in platform engineering for high-traffic e-commerce retail systems. It covers best practices, methods, and frameworks for managing and optimizing platforms with high traffic and flawless user experiences. The report defines platform engineering executives' key duties as connecting technology strategy with business objectives. Platform engineering leaders must be technical, innovative, manage cross-functional teams, and respond to market changes. Leaders must promote continual development and adaptability to maintain high performance in dynamic e-commerce settings. it focuses on the architectural design concepts that support high-traffic e-commerce systems. Leaders must ensure platform design is durable, scalable, and can handle peak traffic without performance deterioration. The article explores microservices architectures, containerization, and cloud-native solutions for scalability and flexibility. Effective data management and caching are also investigated to improve platform performance and user experience. Security is another important paper topic. E-commerce sites are obvious targets for cyberattacks, therefore executives must employ robust security measures. Proactive security, strict access limits, and vulnerability monitoring are best practices. The article emphasizes the need of data protection rules and industry standards to secure consumer data and build confidence.

Towards a Deep Learning-Powered Herbarium Image Analysis Platform

Global digitization efforts have archived millions of specimen scans worldwide in herbarium collections, which are essential for studying plant evolution and biodiversity. ReColNat hosts, at present, over 10 million images. However, analyzing these datasets poses crucial challenges for botanical research. The application of deep learning in biodiversity analyses, particularly in analyzing herbarium scans, has shown promising results across numerous tasks (Ariouat et al. 2023, Ariouat et al. 2024, Groom et al. 2023, Sahraoui et al. 2023). Within the e-Col+project (ANR-21-ESRE-0053), we are developing multiple deep learning models aimed at identifying plant morphological traits. We have developed pipelines and models for cleaning, analyzing, and transforming herbarium images, including models for: i) detecting non-vegetal elements, such as barcodes, envelopes, labels, etc.; ii) detecting plant organs, including leaves, flowers, fruits, etc.; and iii) segmenting to recognize plant parts for image cleaning. We are also developing models for classification tasks related to various morphological traits. To validate these models, improve their generalization, and make them easily usable by end-users, deploying them within a generic platform is crucial. The generic platform called PlantAI, currently under development by the e-Col+ project, should enable easy deployment during development for testing and allow users to load annotations for new traits in order to train a model and add it to the existing catalog. The platform is based on a microservice architecture, allowing users to upload images, create custom datasets, and access various AI models for image analysis. The platform is composed of four main modules, as illustrated in Fig. 1. The first module is the collaborative workspace manager, which allows users to create projects and image datasets and invite other users to collaborate on a project. The second module is the navigation interface and dashboards. This module integrates a search engine using metadata and AI annotations, a navigation interface between projects, datasets, and specimens, as well as dashboards for analysis across datasets, specimens, and AI models. The third module is the dataset manager, which handles metadata and annotations associated with the specimens. These annotations can be produced either by expert users or by AI models. The fourth module is the AI models management module, so that models can be used to generate AI annotations of specimen. During the development lifecycle of an AI model, users can create datasets and annotate them with AI models. These annotations can be in two possible states: validated by experts and non-validated. Users collaborating on a project can indicate errors in the model predictions and leave comments to explain their evaluations. These corrections made by experts can be used to retrain the models and thus improve their performance. This platform, will be highly beneficial for botanists, enhancing the efficiency and effectiveness of biodiversity analyses from herbarium scans. We aim to provide users with a catalog of AI models through this platform and allow them to import their own datasets with their own annotations regarding traits of their choice. Users will be able to select a model from the AI model catalog and train it using their dataset. Ultimately, the model obtained from this training will be automatically deployed to be available for AI annotation. The annotations produced by this model will be automatically available in the filtering and navigation interface, thus allowing for dynamic and automatic integration of the AI annotations into the navigation interface.

Softwarized and containerized microservices-based network management analysis with MSN

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

REVOLUTIONIZING USER EXPERIENCE: ARCHITECTURAL INNOVATIONS IN OVO PAYMENT APPLICATION

In the era of digital transformation, mobile-based payment applications have become an important element in everyday life, changing the way transactions and managing finances. Online payment applications provide significant benefits by making financial transactions easier without cash, providing flexibility in choosing brands, and supporting the progress of fintech businesses and startups in the digital financial sector. Lippo Group, through OVO, presents innovation in electronic money payment systems. OVO's rapid growth lies not only in the ease of financial transactions, but also in its mobile application which is designed to provide a better user experience. This research analyzes architectural innovation in the OVO mobile application, aiming to revolutionize the user experience and increase OVO's competitiveness in the mobile payments industry. Using descriptive qualitative methods, this research utilizes user cards, storyboards, and customer journeys to analyze the implementation of Microservices Architecture and Load Balancing. The results show that this innovation provides significant benefits, improving the system's scalability, reliability and maintenance efficiency. Using Load Balancing is effective in solving balance problems and customer service response. This research provides in-depth insight into the evolution of financial applications in Indonesia.

Implementation and Optimization of Digital Ticket Applications Using Java in Online Sales Systems

Online digital ticket sales have become one of the main methods of distributing tickets for various events, from concerts to transportation. Although many digital ticketing applications are available, there are significant challenges regarding performance, security, and integration with payment systems. This research aims to implement and optimize a digital ticket application using Java in an online sales system to increase efficiency and security. This research methodology includes the development of Java-based applications with a microservices architecture approach, which allows separation of functionality and increases scalability. This application is designed to manage the ticket purchasing process, including event selection, payment, and electronic ticket delivery using QR Code technology for verification. In the implementation process, Spring Boot technology was used for server-side development and JavaFX for the user interface. The results of this research show that the Java-based digital ticket application developed can manage transactions efficiently and safely, and can handle high user volumes without significant performance degradation. These findings provide an important contribution in the field of digital ticket application development and can be used as a reference for implementing similar systems in various sectors.

Circuit Breaker

In the realm of microservices architecture, ensuring reliability and resilience is paramount due to the distributed nature and interdependencies of services. A key pattern employed to enhance system robustness is the circuit breaker. When a microservice experiences failures, the circuit breaker pattern detects these faults and transitions between three states: closed, open, and half-open. During this period, requests are automatically redirected or fail fast, preventing additional strain on the troubled service. Once the service stabilizes, the circuit breaker transitions to the half-open state, where it allows a limited number of test requests to verify recovery. If these succeed, the circuit breaker closes, restoring normal operation. This abstract explores the implementation, benefits, and challenges of circuit breakers in microservices, emphasizing their role in maintaining high availability and robust performance in modern distributed systems.

A Novel Rational Medicine Use System Based on Domain Knowledge Graph

Medication errors, which could often be detected in advance, are a significant cause of patient deaths each year, highlighting the critical importance of medication safety. The rapid advancement of data analysis technologies has made intelligent medication assistance applications possible, and these applications rely heavily on medical knowledge graphs. However, current knowledge graph construction techniques are predominantly focused on general domains, leaving a gap in specialized fields, particularly in the medical domain for medication assistance. The specialized nature of medical knowledge and the distinct distribution of vocabulary between general and biomedical texts pose challenges. Applying general natural language processing techniques directly to the medical domain often results in lower accuracy due to the inadequate utilization of contextual semantics and entity information. To address these issues and enhance knowledge graph production, this paper proposes an optimized model for named entity recognition and relationship extraction in the Chinese medical domain. Key innovations include utilizing Medical Bidirectional Encoder Representations from Transformers (MCBERT) for character-level embeddings pre-trained on Chinese biomedical corpora, employing Bi-directional Gated Recurrent Unit (BiGRU) networks for extracting enriched contextual features, integrating a Conditional Random Field (CRF) layer for optimal label sequence output, using the Piecewise Convolutional Neural Network (PCNN) to capture comprehensive semantic information and fusing it with entity features for better classification accuracy, and implementing a microservices architecture for the medication assistance review system. These enhancements significantly improve the accuracy of entity relationship classification in Chinese medical texts. The model achieved good performance in recognizing most entity types, with an accuracy of 88.3%, a recall rate of 85.8%, and an F1 score of 87.0%. In the relationship extraction stage, the accuracy reached 85.7%, the recall rate 82.5%, and the F1 score 84.0%.

Microservices-Based Resource Provisioning for Multi-User Cloud VR in Edge Networks

Cloud virtual reality (VR) is attracting attention in terms of its lightweight head-mounted display (HMD), providing telepresence and mobility. However, it is still in the research stages due to motion-to-photon (MTP) latency, the need for high-speed network infrastructure, and large-scale traffic processing problems. These problems are expected to be partially solved through edge computing, but the limited computing resource capacity of the infrastructure presents new challenges. In particular, in order to efficiently provide multi-user content such as remote meetings on edge devices, resource provisioning is needed that considers the application’s traffic patterns and computing resource requirements at the same time. In this study, we present a microservice architecture (MSA)-based application to provide multi-user cloud VR in edge computing and propose a scheme for planning an efficient service deployment considering the characteristics of each service. The proposed scheme not only guarantees the MTP latency threshold for all users but also aims to reduce networking and computing resource waste. The proposed scheme was evaluated by simulating various scenarios, and the results were compared to several studies. It was confirmed that the proposed scheme represents better performance metrics than the comparison schemes in most cases from the perspectives of networking, computing, and MTP latency.