Microservices Research Articles

$$\mu $$XL: explainable lead generation with microservices and hypothetical answers

AbstractLead generation refers to the identification of potential topics (the ‘leads’) of importance for journalists to report on. In this article we present $$\mu $$ μ XL, a new lead generation tool based on a microservice architecture that includes a component of explainable AI. $$\mu $$ μ XL collects and stores historical and real-time data from web sources, like Google Trends, and generates current and future leads. Leads are produced by a novel engine for hypothetical reasoning based on temporal logical rules, which can identify propositions that may hold depending on the outcomes of future events. This engine also supports additional features that are relevant for lead generation, such as user-defined predicates (allowing useful custom atomic propositions to be defined as Java functions) and negation (needed to specify and reason about leads characterized by the absence of specific properties). Our microservice architecture is designed using state-of-the-art methods and tools for API design and implementation, namely API patterns and the Jolie programming language. Thus, our development provides an additional validation of their usefulness in a new application domain (journalism). We also carry out an empirical evaluation of our tool.

A Systematic Literature Review on The Strategic Shift to Cloud ERP: Leveraging Microservice Architecture and MSPs for Resilience and Agility

The COVID-19 pandemic has necessitated profound changes in the business and technology landscapes, compelling organizations to reassess their Enterprise Resource Planning (ERP) systems. Traditional ERP systems have demonstrated significant limitations in agility, scalability, and resilience, prompting a strategic shift towards cloud-based ERP solutions. This systematic literature review (SLR) aims to critically evaluate the transformation of ERP systems through the adoption of Microservice Architecture (MSA) and the integration of Managed Service Providers (MSPs), highlighting their role in enhancing system flexibility and operational continuity in a post-pandemic world. We conducted a systematic analysis of 124 scholarly articles published since 2010 to compare traditional ERP systems with MSA-based Cloud ERP solutions. Key insights reveal that MSA significantly improves system modularity and adaptability, addressing the shortcomings of monolithic architectures. Additionally, MSPs offer crucial support in managing the complexities of cloud transitions, ensuring security and efficiency. Our findings underscore the importance of a holistic approach to ERP modernization, integrating technological advancements with strategic business objectives. This study not only fills a critical gap in the literature but also provides actionable recommendations for practitioners and policymakers aiming to enhance ERP systems’ resilience and agility. Future research directions are proposed to further explore the synergistic potential of cloud ERP, MSA, and MSPs in fostering innovative and sustainable business practices.

Microservice-Based Vehicular Network for Seamless and Ultra-Reliable Communications of Connected Vehicles

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks.

ChatOps for microservice systems: A low-code approach using service composition and large language models

The Microservice Architecture (MSA) plays a pivotal role in contemporary e-business, promoting service independence, autonomy, and continual evolution in line with the principles of DevOps. However, the distributed nature of the MSA introduces additional complexity, which requires familiarity with multiple DevOps (Development and Operations) tools, thereby increasing the learning curve. This paper presents a specialized ChatOps (Chat Operations) approach that allows MSA developers to compose new ChatOps capabilities in a low-code way (i.e., with minimal coding). The proposed ChatOps4Msa approach leverages established ChatOps functionalities to facilitate the real-time monitoring of service status, conduct service testing, track issues, and receive alerts using natural language or the proposed ChatOps Query Language (CQL). The use of large language models (LLMs) for functional intents also enhances the usability of the DevOps toolchain in microservices systems to streamline implementation.

Microservices Architecture in Point of Sales Application Based on Restful API and Webhook

Microservices Architecture in Point of Sales Application Based on Restful API and Webhook

Архитектурные подходы и технологические решения в создании инновационных веб-приложений для голосовых и текстовых чатов. Анализ, перспективы и реализация

The article provides a detailed examination of the technologies used in an innovative service that implements voice and text chats as a web application, using an already implemented solution as an example. The main focus is on analyzing the architectural and technological solutions employed in the development of innovative web applications for voice and text chats. The article extensively discusses the functional capabilities, scalability of such systems. The service, provisionally named “Voice and Text Chat Service in the Context of Import Substitution”, serves as the object of analysis. Despite its functional similarities and user experience with leading communication platforms such as Discord, Telegram, and VKontakte, it is developed with specific requirements and the latest technologies in web development in mind. The article meticulously reviews the key components of the system, including server logic, client applications, and data infrastructure. Special attention is given to the integration of modern technologies into real products, including the use of cloud services, microservices architecture, and contemporary frameworks and protocols. The analysis covers not only technical aspects but also issues of usability, accessibility, and inclusivity, enabling the creation of more effective services that meet user needs. The article also highlights potential directions for further development of such platforms and presents examples of best practices in this field.

Distributed Transactions in Microservice Architecture: Informed Decision-making Strategies

The emergence of microservice architecture has revolutionized software development practices by decentralizing components, facilitating scalability, and enabling agility in system design and deployment. There are some benefits of incorporating microservices instead of a single server, however, distributed components introduce extra constraints and complexities in maintaining data consistency as well. As microservices interact independently, coordinating data updates across multiple services becomes challenging, particularly in scenarios where transactional integrity is required. Distributed transactions are one of the solutions for ensuring data consistency across services. Regardless of effectiveness distributed transactions entail different trade-offs and performance implications. Those trade-offs are not always justified. This study highlights the need for a nuanced understanding of distributed transactions in microservices by revisiting challenges in managing distributed transactions within data storage systems. It also represents existing solutions to the different distributed transaction methods. In this paper, through experiments comparing microservices and monolithic systems, the impact of distributed transactions on system performance is evaluated, giving intuition about consequences when a single data source transaction is migrated to the distributed environment. This research also contributes to enhancing understanding and decision-making regarding the utilization of distributed transactions in a microservices architecture. Ultimately, this paper presents an optimized decision-framework for the application of distributed transactions in microservices architecture, aiming to simplify and expedite processes of software architecture for software engineers, solution architects, and developers.

Management and Operator Control System based on Microservice Architecture and Application on the HIPr Accelerator

The paper describes the basic principles of developing a distributed control system (DCS) and a GARNET operator control system based on a microservice architecture as part of a high-availability cluster. The application of the operator’s control system as a DCS component is described. The main elements of software components of operator control and DCS are presented and described. The process of conveyor assembly and publication of software tools into a working product environment, which implements the principle of continuous integration, is described. The mechanism of interaction of key components among themselves is presented. The mechanism for hosting management services using the Docker containerization system and Kubernetes container orchestration is demonstrated. Examples of services for interaction with users in the environment of the GARNET operator control system being developed, separation of users by roles and access rights, integration of the data visualization service using Grafana are shown. The vector of further development of DCS and operator control tools is described, in particular, the possibility of using the practice of developing user web interfaces using the micro frontend approach. The components and results of the operation of a prototype system designed to interact with the measurement infrastructure of the linear heavy ion accelerator HIPR are presented.

Enhancing Autonomous Driving Robot Systems with Edge Computing and LDM Platforms

The efficient operation and interaction of autonomous robots play crucial roles in various fields, e.g., security, environmental monitoring, and disaster response. For these purposes, processing large volumes of sensor data and sharing data between robots is essential; however, processing such large data in an on-device environment for robots results in substantial computational resource demands, causing high battery consumption and heat issues. Thus, this study addresses challenges related to processing large volumes of sensor data and the lack of dynamic object information sharing among autonomous robots and other mobility systems. To this end, we propose an Edge-Driving Robotics Platform (EDRP) and a Local Dynamic Map Platform (LDMP) based on 5G mobile edge computing and Kubernetes. The proposed EDRP implements the functions of autonomous robots based on a microservice architecture and offloads these functions to an edge cloud computing environment. The LDMP collects and shares information about dynamic objects based on the ETSI TR 103 324 standard, ensuring cooperation among robots in a cluster and compatibility with various Cooperative-Intelligent Transport System (C-ITS) components. The feasibility of operating a large-scale autonomous robot offloading system was verified in experimental scenarios involving robot autonomy, dynamic object collection, and distribution by integrating real-world robots with an edge computing–based offloading platform. Experimental results confirmed the potential of dynamic object collection and dynamic object information sharing with C-ITS environment components based on LDMP.

Software compliance in various industries using CI/CD, dynamic microservices, and containers

Abstract The microservices architecture is widely used in modern businesses due to its ability to offer speed, efficiency, adaptability, autonomy, and usability. On the other hand, this architectural paradigm demands a well-designed infrastructure for optimal container and cluster utilization. Establishing version control and a solid continuous integration/continuous deployment (CI/CD) infrastructure becomes critical for accelerating software delivery to production and ensuring code alignment with best practices. This scientific investigation investigates the development of customized, specialized CI/CD procedures for software compliance, expanding its scope beyond traditional software delivery to include the complexities of design, testing, and server deployment. By integrating real-world examples from industry and reviewing crucial tools, the goal is to provide organizations with empirical information to navigate the difficulties of modern software development. The upcoming research seamlessly fits into the larger discussion, providing a deep understanding of sophisticated structures and their design. This interdisciplinary research combines the scientific principles governing microservices with the practical specifics of CI/CD methodologies, giving businesses a thorough understanding and practical insight into the tools needed to navigate the diverse landscape of modern software engineering. Among the main findings of this research is a suggestion for a new approach known as General repository compliance operations.

MICROSERVICE ARCHITECTURE FOR CYBER-PHYSICAL SYSTEMS

Cyber-Physical Systems connect the physical and digital worlds. They are composed of hardware connected to the physical world, software, and potentially other types of systems. They are utilized across various industries, including robotics, healthcare, smart cities, automotive, industry, and space. These systems are very complex to design and implement. Cloud computing technologies provide an excellent environment for the Cyber-Physical Systems software and tools for maintaining and scaling the infrastructure. One of the main challenges is how to design cloud systems properly for Cyber-Physical Systems. Currently, microservice architecture is broadly used for software in the cloud. At its core, Microservices provide flexibility, availability, scalability, and independence of modules, as well as agile development and deployment processes. These advantages are well-aligned with the needs of Cyber-Physical Systems. However, many challenges still exist in implementing a microservice architecture for Cyber-Physical Systems. The challenges include complex distributed system networking, real-time data processing, microservice software architecture, microservice availability, and reliability of the system components. This paper provides a study whose primary goal is to give the main microservice architectural principles and patterns used, summarize the advantages and challenges, and improve the knowledge of the microservice architecture used for Cyber-Physical Systems. At first, a literature review of modern research papers was conducted. Then, each paper was analyzed. A summary of all selected research papers was produced. The results and conclusion sections deliver the summaries and give future research directions.

Designing Claim Systems in Health Insurance Companies with Microservices and Event-Driven Architecture Approach

Through digital transformation, insurance companies, especially in the health sector, are increasingly adopting modern technologies to enhance efficiency and service quality. Health insurance allows individuals or families to mitigate the financial risks associated with high and unexpected medical expenses. One crucial area is insurance claim, where a fast and accurate process is key to customer satisfaction. This study proposes the design and architecture of an insurance claim system using a microservices and event-driven approach. This approach enables insurance companies to break down applications into separate components, facilitating scalability, flexibility, and easier maintenance. Additionally, with an event-driven approach, the system can quickly respond to changes and events in the business environment. A comprehensive analysis shows that implementing microservices and event-driven architecture in the insurance claim system can enhance overall system performance, scalability, and resilience. For insurance companies, adopting microservices and event-driven architecture can lead to increased operational efficiency, reduced time to market for new products, and improved customer experiences through faster claim processing. Policyholders will benefit from quicker claim resolutions and a more transparent and responsive claim process. This study provides valuable insights for health insurance companies looking to upgrade their IT infrastructure to meet future challenges. The findings from this research will be documented to support the development of insurance business technology, specifically for health insurance claims in Indonesia.

HeMiRCA: Fine-Grained Root Cause Analysis for Microservices with Heterogeneous Data Sources

Microservices architecture improves software scalability, resilience, and agility but also poses significant challenges to system reliability due to their complexity and dynamic nature. Identifying and resolving anomalies promptly is crucial because they can quickly propagate to other microservices and cause severe damage to the system. Existing root-cause metric localization approaches rely on metrics or metrics-anomalies correlations but overlook other monitoring data sources ( e.g. , traces). We are the first to identify and leverage the anomaly-aware monotonic correlation between heterogeneous monitoring data, motivated by which we propose a novel framework, HeMiRCA , for hierarchical root cause analysis using Spearman correlation. HeMiRCA is based on the key observation that the microservice responsible for a particular type of fault exhibits a monotonic correlation between the trends of its associated metrics and the trace-based anomaly score of the system. HeMiRCA first calculates time-series anomaly scores using traces and then exploits the correlations between multivariate metrics and the scores to rank the suspicious metrics and microservices. HeMiRCA has been evaluated on two datasets collected from widely used microservice systems. The results show that HeMiRCA outperforms the state-of-the-art approaches by a large margin in identifying root causes at both service level and metric level, achieving a top-1 hit ratio of 82.7% and 74% on average, respectively.

Methods for detecting anomalies in microservices using statistical analysis

The subject of the study is methods of detecting anomalies in microservices using statistical analysis. Microservices is a popular software development architecture that allows for flexible and scalable systems. However, due to their complexity, such systems can be vulnerable to various types of anomalies that can affect their performance and reliability. The goal of the work is an analytical review of existing methods of detecting anomalies in microservice systems using statistical analysis methods. Detection of anomalies is critical to ensure stable system operation and quick response to possible problems. To achieve the purpose, the following tasks are defined: review of methods for detecting anomalies in microservices; description of the principles of regression analysis, cluster analysis and the method of principal components; comparison of methods according to the criteria of efficiency, computational complexity, resistance to noise and adaptability; recommendations for choosing a method and the possibility of combining them; summary of results and identification of directions for future research. A method for detecting anomalies in microservices is considered, which includes regression analysis, cluster analysis, and the method of principal components (PCA). The results of the study confirmed that each method has its advantages and limitations. Regression analysis is effective in systems with clear trends, but less effective in complex and dynamic systems. Cluster analysis has proven to be robust to noise and capable of detecting both individual anomalies and groups of anomalous events but requires significant computational resources. The method of principal components (PCA) is a powerful tool for the analysis of high-dimensional data, but it has limitations in the high complexity of calculations and interpretation of results. Each of the considered methods has its pros and cons, so the study proposed a new method that would consist in combining them. The conclusions emphasize the importance of statistical analysis for monitoring microservice systems. Well-chosen data analysis techniques facilitate the detection of anomalies in complex environments such as microservices. The use of regression analysis, cluster analysis and the method of principal components allows you to get a deep insight into the operation of the system. However, for best results, it is recommended to combine different methods and analyze their results in the context of a specific system. This approach provides greater resistance to anomalies and faster response to them in microservice architectures.

Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models.

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

A fuzzy logic-based quality model for identifying microservices with low maintainability

Microservice Architecture (MSA) is a popular architectural style that offers many advantages regarding quality attributes, including maintainability and scalability. Developing a system as a set of microservices with expected benefits requires a quality assessment strategy that is established on the measurements of the system's properties. This paper proposes a hierarchical quality model based on fuzzy logic to measure and evaluate the maintainability of MSAs considering ISO/IEC 250xy SQuaRE (System and Software Quality Requirements and Evaluation) standards. Since the qualitative bounds of low-level quality attributes are inherently ambiguous, we use a fuzzification technique to transform crisp values of code metrics into fuzzy levels and apply them as inputs to our quality model. The model generates fuzzy values for the quality sub-characteristics of the maintainability, i.e., modifiability and testability, converted to numerical values through defuzzification. In the last step, using the values of the sub-characteristics, we calculate numerical scores indicating the maintainability level of each microservice in the examined software system. This score was used to assess the quality of the microservices and decide whether they need refactoring. We evaluated our approach by creating a test set with the assistance of three developers, who reviewed and categorized the maintainability levels of the microservices in an open-source project based on their knowledge and experience. They labeled microservices as low, medium, or high, with low indicating the need for refactoring. Our method for identifying low-labeled microservices in the given test set achieved 94% accuracy, 78% precision, and 100% recall. These results indicate that our approach can assist designers in evaluating the maintainability quality of microservices.

Performance evaluation of microservices communication with REST, GraphQL, and gRPC

Microservice architecture has become the design paradigm for creating scalable and maintainable software systems. Selecting the proper communication protocol in microservices is critical to achieving optimal system performance. This study compares the performance of three commonly used API protocols: REST, GraphQL, and gRPC, in microservices architecture. In this study, we established three microservices implemented in three containers and each microservice contained a Redis and MySQL database. We evaluated the performance of these API protocols using two key performance metrics: response time and CPU Utilization. This study performs two distinct data retrieval: fetching flat data and fetching nested data, with a number of requests ranging from 100 to 500 requests. The experimental results indicate that gRPC has a faster response time, followed by REST and GraphQL. Moreover, GraphQL shows higher CPU Utilization compared to gRPC and REST. The experimental results provide insight for developers and architects seeking to optimize their microservices communication protocols for specific use cases and workloads.

Design and Implementation of an Intelligent Campus Second-hand Trading Platform based on Microservices and Cloud Computing

With the rapid development of smart campuses, traditional campus second-hand trading methods can no longer meet the growing needs of students. This paper proposes a design scheme for an intelligent campus second-hand trading platform based on microservices and cloud computing, which provides customized trading experience for users by introducing advanced recommendation algorithms. The microservice architecture gives the platform extremely high module decoupling and autonomous scalability, ensuring the high availability and agile response of the system. The recommendation system uses a hybrid algorithm, combining content filtering and collaborative filtering, and continuously optimizes the recommendation results through machine learning, so as to accurately capture user preferences. One of the features of the platform is its community-driven data feedback mechanism, which not only can adjust the recommendation strategy in real-time but also can promote the sustainable development of campus economy and culture through data mining. In addition, the research explores a novel credit evaluation system design to establish a trustworthy trading environment. The experimental results show the technical innovation and high efficiency of the platform in multiple dimensions, which is significantly superior to the existing solutions. The successful implementation of this research indicates that the campus trading platform is moving towards a more intelligent and personalized direction, and is expected to play a key role in the smart campus ecosystem.

MicroIRC: Instance-level Root Cause Localization for Microservice Systems

The use of microservice architecture is gaining popularity in the development of web applications. However, identifying the root cause of a failure can be challenging due to the complexity of interconnected microservices, long service invocation links, dynamic changes in service states, and the abundance of service deployment nodes. Furthermore, as each microservice may have multiple instances, it can be difficult to identify instance-level failures promptly and effectively when the microservice topology and failure types change dynamically. To address this issue, we propose MicroIRC (Instance-level Root Cause Localization for Microservice Systems), a novel metrics-based approach that localizes root causes at the instance level while exhibiting robustness to adapt to dynamic changes in topology and new types of anomalies. We begin by training a graph neural network to fit different root cause types based on extracted time series features of microservice system metrics. Next, we construct a heterogeneous weighted topology (HWT) of microservice systems and execute a personalized random walk to identify root cause candidates. These candidates, along with real-time metrics from the anomalous time window, are then fed into the trained graph neural network to generate a ranked root cause list. Experiments conducted on five real-world datasets demonstrate that MicroIRC can accurately locate the root cause of microservices at the instance level, achieving a precision rate of 93.1% for the top five results. Furthermore, compared to the state-of-the-art methods, MicroIRC can improve the accuracy of root cause localization by more than 17% at the service level and more than 11.5% at the instance level. Remarkably, it exhibits robustness in scenarios involving new failure types, achieving an accuracy of 84.2% for the top result amid dynamic topological changes.

Energy-aware dynamic response and efficient consolidation strategies for disaster survivability of cloud microservices architecture

Energy-aware dynamic response and efficient consolidation strategies for disaster survivability of cloud microservices architecture