Service-oriented Architecture Research Articles

Modular Web-based Learning Model to Address Underdeveloped ICT Infrastructure for Smart E-learning Education System

Advancements in Information and Communication Technology (ICT) enhance learning and instructional practices, generating chances for novel techniques and tools while simultaneously transforming the learning model. Historically, smart education models mostly consisted of qualitative investigations that delineated the conceptual structure and its implications for the Smart E-learning Education System (SEES). While these theoretically organized SEES can enhance student learning, facilitate peer and instructor communication, and improve teaching practices—thereby aiding in the comprehension of the status of learners and needs and offering real-time synchronous/asynchronous guidance—many qualitative investigations conducted in the past lack empirical evidence to substantiate the prioritization of E-Learning scheduling. This research first examined the technological barriers to implementing the SEES in developing nations. As a result, the suggested framework for the SEES based on underdeveloped ICT has been introduced. The system utilizes an integrated online/offline Web-Based Learning Model (WLM) incorporating a Service-oriented Architecture (SA) architecture to create SEES. The online mode is enabled for a WLM when an internet connection is accessible. The offline variant of the system is provided in a packed format and is used when Internet access is unavailable in an isolated classroom. A working example was launched to get input from learners and instructors across diverse educational levels. The trial installation and survey results confirm the viability and benefits of the suggested system.

Save A Life Maps-Traffic Clearance System for Emergency Services

ABSTRACT The Save a Life (SAL) Maps addresses the challenge of traffic congestion affecting emergency services and provides swift response during life-critical situations. A path clearance algorithm is integrated into an application, which identifies the most optimal routes enabling emergency services to navigate efficiently. The algorithm incorporates a mechanism that uses micro services to facilitate path clearance for emergency services, provide fastest routes, assist general public in accessing emergency services, and notify the traffic police during high traffic congestion to assist by manually clearing out the path. Docker is employed to containerize the micro services, allowing for efficient deployment and management on the Google Kubernetes Engine platform. This micro services-based algorithm achieves enhanced efficiency, optimal resource utilization, real-time decision-making, scalability, adaptability and offers a reliable solution which highlight the importance of adopting path clearance algorithms and micro services architecture for ambulances enabling a more agile and responsive system.

Design of a microservices-based architecture for residential energy efficiency monitoring

With the significant advancement of electrical infrastructure in the context of smart buildings and smart homes, the need arises to overcome the limitations of the traditional energy efficiency control system based on service-oriented architecture (SOA). To address these challenges, this study proposes a distributed architecture based on microservices, with the main objective of improving the performance and stability of these systems. This proposal seeks to enable end users to effectively monitor and control their electrical devices while effectively integrating them into a wide network of power systems. The proposed architecture relies on a series of cloud services that enable better performance and control in energy efficiency management, highlighting key features of microservices such as fault tolerance, performance, and scalability. Using a structural methodology centered on pre-existing components and an iterative approach, a versatile and scalable architecture was designed that addresses current challenges in energy efficiency management. The results show a significant impact on key performance indicators such as demand response, energy savings, and power quality, highlighting the resilience and scalability of the proposed architecture. The conclusions highlight the importance of energy efficiency in reducing the environmental impact and costs associated with electric power, suggesting future improvements in data access and the implementation of advanced machine learning algorithms.

Optimization of datacenter selection through a genetic algorithm-driven service broker policy

Establishing an optimal datacenter selection policy within the cloud environment is paramount to maximize the performance of the cloud services. Service broker policy governs the selection of datacenters for user requests. In our research, we introduce an innovative approach incorporating the genetic algorithm with service broker policy to assist cloud services in identifying the most suitable datacenters for specific userbases. The effectiveness of our proposed genetic algorithm was rigorously evaluated through experiments conducted on CloudAnalyst platform. The results clearly indicate that our proposed algorithm surpasses existing service broker policies and previous research works done in this field in terms of reducing response time and data processing time. The results analysis validates its efficacy and potential for enhancing cloud service performance and reducing the cost of overall cloud infrastructure.

Comprehensive Analysis of Cloud Computing Performance Factors: Investigating the Impact of Response Time, Load Balancing and Service Broker Policies on Cloud Service Efficiency Using CloudSim Simulation

Cloud performance refers to the efficiency and effectiveness with which a cloud system operates delivering hosted services over the internet. As cloud computing continues to offer flexibility, scalability and computational power monitoring and improving cloud performance is essential. Performance optimization is influenced by factors such as load balancing and service broker policies which impact system response times and overall user experience. This paper provides an in-depth review of key publications and real-time cloud performance tools identifying critical performance factors that affect cloud efficiency. Notably, response time emerged as a fundamental metric for cloud service quality. Using CloudSim simulation we examine cloud performance evaluation criteria and experimentally assess the impact of response time dependencies on broker policies, load balancing techniques and data center distribution. This study offers a framework for understanding cloud performance evaluation and highlights strategies to enhance user experience in diverse cloud environments.

Computing Unit and Data Migration Strategy under Limited Resources: Taking Train Operation Control System as an Example

There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained conditions in this paper. By decomposing and reallocating application functions, optimizing the use of CPU, memory, and network bandwidth, a hierarchical structure of computing units is proposed. The architecture divides the system into layers and components to facilitate resource management. Then, a migration strategy is proposed, which mainly focuses on moving components and data from less critical paths to critical paths and ultimately optimizing the utilization of computing resources. Specifically, the test results suggest that the method can reduce the overall CPU utilization by 27%, memory usage by 6.8%, and network bandwidth occupation by 35%. The practical value of this study lies in providing a theoretical model and implementation method for optimizing resource allocation in scenarios where there is a gap between resource and computing requirements in fixed-resource service architectures. The strategy is compatible for distributed computing architectures and cloud/cloud–edge-computing architectures.

Reconstruction and analyses of genome-scale halomonas metabolic network yield a highly efficient PHA production

In pursuit of reliable and efficient industrial microbes, this study integrates cutting-edge systems biology tools with Halomonas bluephagenesis TD01, a robust halophilic bacterium. We generated the complete and annotated circular genome sequence for this model organism, constructed and meticulously curated a genome-scale metabolic network, achieving striking 86.32% agreement with Biolog Phenotype Microarray data and visualize the network via an interactive Electron/Thrift server architecture. We then analyzed the genome-scale network using vertex sampling analysis (VSA) and found that productions of biomass, polyhydroxyalkanoates (PHA), citrate, acetate, and pyruvate are mutually competing. Recognizing the dynamic nature of H. bluephagenesis TD01, we further developed and implemented the hyper-cube-shrink-analysis (HCSA) framework to predict effects of nutrient availabilities and metabolic reactions in the model on biomass and PHA accumulation. We then, based on the analysis results, proposed and validate multi-step feeding strategies tailored to different fermentation stages. This integrated approach yielded remarkable results, with fermentation culminating in a cell dry weight of 100.4 g/L and 70% PHA content, surpassing previous benchmarks. Our findings exemplify the powerful potential of system-level tools in the design and optimization of industrial microorganisms, paving the way for more efficient and sustainable bio-based processes.

Flexible hybrid post-quantum bidirectional multi-factor authentication and key agreement framework using ECC and KEM

Post-quantum computing becomes a real threat in the coming years, resulting in vulnerable security protocols that rely on traditional public key algorithms. It is not evident to provide protection against it in a cost-efficient manner, especially for Internet of Things (IoT) devices with limited capabilities. There is a high variety of IoT applications, some require only short-term security (e.g. agriculture) and others long-term security (e.g. healthcare). In order to provide a unified security approach for such heterogenity in IoT, we propose a flexible hybrid authentication and key agreement framework for a client–server architecture, which relies both on the classical elliptic curve cryptography (ECC) and on a quantum secure key encapsulation mechanism (KEM). There are five versions that can be derived from the framework, going from a fully hybrid, and partial hybrid to classical construction. The trade-off between performance and security strength is demonstrated for each of these versions. The overall cost of the protocols is highly reduced thanks to the usage of multifactors in the authentication process, both on the user side by means of biometrics and the device side by means of physically unclonable functions (PUFs). We show that both Kyber and Mc Elience as KEM can offer reasonable performance, depending on the situation. The unified framework offers optimal security protection against the most well-known attacks.

Microservices Patterns Recommendation based on Information Retrieval

Software developers encounter recurring problems during software development, which can be solved using proven solutions known as design patterns. Microservices architecture, a Service-Oriented Architecture (SOA) variant, presents common communication, deployment, and service definition challenges. However, selecting the appropriate design pattern from a vast pool of patterns to solve a problem is difficult for novice and experienced developers. This paper proposes a recommendation tool based on Information Retrieval (IR) to assist developers in choosing the suitable microservices pattern to solve a given problem. The tool leverages textual descriptions given by developers to provide relevant indications of microservices patterns. It has been evaluated using both toy and industrial problems, demonstrating promising results. The results showed that the tool was able to solve 60% of toy design problems, indicating that it can provide valuable and accurate recommendations. Furthermore, tests with industrial problems revealed that over 70% of the recommended patterns helped to address the problems at hand. Interviews with developers who work in the software industry corroborated the recommendation tool’s effectiveness and practicality.

Design an Agricultural Soil and Environment Monitoring System Based on IoT

One of the problems farmers face is the inability to make complete, real-time, and accurate observations of their farmland. The system proposed in this paper helps farmers to know the condition of farmland from anywhere and anytime by using a web-based application. The main objective of this prototype is to reduce the failure of the growth process of farming commodities by knowing the conditions inside and outside the soil with a total of 14 parameters. Internet of Things (IoT) technology is used to implement the prototype, which consists of Sensor Panels, Controllers, Message Broker, and Backend Service. All obtained data, created and tested in real-time, are displayed on the application. In addition to real-time data display, the system also includes monitoring history, alerts, and site location management.

A node deployment and resource optimization method for CPDS based on cloud‐fog‐edge collaboration

AbstractWith the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption.

The Community Solid Server: Supporting research & development in an evolving ecosystem

The Solid project aims to empower people with control over their own data through the separation of data, identity, and applications. The goal is an environment with clear interoperability between all servers and clients that adhere to the specification. Solid is a standards-driven way to extend the Linked Data vision from public to private data, and everything in between. Multiple implementations of the Solid Protocol exist, but due to the evolving nature of the ecosystem, there is a strong need for an implementation that enables qualitative and quantitative research into new features and allows developers to quickly set up varying development environments. To meet these demands, we created the Community Solid Server, a modular server that can be configured to suit the needs of researchers and developers. In this article, we provide an overview of the server architecture and how it is positioned within the Solid ecosystem. The server supports many orthogonal feature combinations on axes such as authorization, authentication, and data storage. The Community Solid Server comes with several predefined configurations that allow researchers and developers to quickly set up servers with different content and backends, and can easily be modified to change many of its features. The server will help evolve the specification, and support further research into Solid and its possibilities.

Exploring emergent microservice evolution in elastic deployment environments

Microservices have become an important technology to enable the dynamic composition of large-scale self-adaptive systems. Although modern microservice ecosystems provide a variety of autonomous adaptation mechanisms, when focusing on the microservice itself, they can only account for changes in the sheer increase in workload volume. On the other hand, when workload patterns change, efficient treatment requires the intervention of DevOps experts to manually evolve the internal architecture of services. Given the need to quickly adapt systems to respond to changes, solely relying on DevOps to react to workload pattern changes becomes a bottleneck for future systems. To address this issue, we advance the concept of emergent microservices, that autonomously adapt and evolve their internal architectural composition to better handle changes in the pattern of incoming requests without human intervention. We demonstrate the effectiveness of our approach by exploring this novel concept in the context of a microservice-based Smart City platform.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Backlash or victims? ‘Victimised Korean husbands’ of migrant wives and problematic norms on sexual relations in South Korea

Violence against marriage migrants became a critical social issue in South Korea in the late 2000s, and the government instituted various measures in response, partially adopting civil society organisations’ demands to protect migrant women. However, men’s rights groups also mobilised shortly after to advocate for the rights of citizen-husbands under the slogan of ‘male victims of international marriages’. Their campaigns targeted both the unscrupulous commercial matchmakers and their deceitful migrant wives. This article situates the victimised men discourse as a backlash against violence against women campaigns to reverse policy gains in the protection of women’s rights. The article argues that government policies have legitimised such discourse and used flawed ‘data’ to justify their policies. This article uses as data complaints submitted to the Korea Consumer Agency, a government regulator for consumer disputes, about international marriages and focuses on their mentioning of sexual relations, intimacy and sexuality. By doing so, this article demonstrates that men’s rights as consumers of marriage brokerage services are behind their claims of victimhood, and the victimhood discourse undermines the safety and human rights of migrant women. Moreover, a close look at the database about ‘male victimhood’ reveals severe human rights violations against migrant women.

A complete client–server architecture providing prevention from insider attack using homogeneous multi-core RSA crypto-processor

A complete client–server architecture providing prevention from insider attack using homogeneous multi-core RSA crypto-processor

Data Mesh: A Systematic Gray Literature Review

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has piqued the practitioners’ interest, and considerable gray literature exists. At the same time, we observe a lack of academic attempts at defining and building upon the concept. Hence, in this article, we aim to start from the foundations and characterize the data mesh architecture regarding its design principles, architectural components, capabilities, and organizational roles. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The resulting review provides insights into practitioners’ perspectives on the four key principles of data mesh: data as a product, domain ownership of data, self-serve data platform, and federated computational governance. Moreover, due to the comparability of data mesh and SOA (service-oriented architecture), we mapped the findings from the gray literature into the reference architectures from the SOA academic literature to create the reference architectures for describing three key dimensions of data mesh: organization of capabilities and roles, development, and runtime. Finally, we discuss open research issues in data mesh, partially based on the findings from the gray literature.

Comparison of Cloud Computing, SaaS, SOA and Microservices

A network application is an application that runs on a single host and facilitates communication with another application running on a different computer or host. Numerous network applications have been introduced, including cloud computing, SaaS, SOA, and microservices. However, these technologies have often been used interchangeably, leading to confusion among developers and researchers. Therefore, the objective of this study is to review and compare the available network applications, specifically cloud computing, Software as a Service (SaaS), Service Oriented Architecture (SOA), and microservices. This study conducts a comparative analysis by examining several important criteria such as definition, type, language, interoperability, characteristics, and examples. By providing clear and precise definitions for each technology, the study aims to clarify their distinctions and appropriate usage contexts. The findings successfully establish the correct terminology for each technology and offer detailed discussions on the specific platforms and situations where each is most suitable. This comprehensive review serves as a valuable resource for developers and researchers seeking to navigate the complexities of modern network applications.

Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

Application of Real-Time Data Service Technology in Smart oilfield

Abstract Real time data services play an important role in the construction of Smart oilfields, which can help oilfield production managers better understand the production situation and make more accurate decisions. Real time data services based on the Data Lake platform will bring more efficient and safer production methods to oil and gas fields, promoting the upgrading and sustainable development of the Smart oilfield industry. The technical architecture of Smart oilfield real-time data service mainly includes data lake platform, big data processing, edge computing and other core technologies. The data lake platform provides infrastructure, platform services and application services for Smart oilfield. Big data processing technology is responsible for rapid and accurate analysis of massive data. edge computing refers to computing at the data source to reduce data transmission and improve computing efficiency. The key technologies include data collection, transmission, storage, analysis, etc. Among them, data collection mainly uses sensors, PLCs and other equipment to collect production data. Data transmission uses technologies such as the Internet of Things and industrial Ethernet to transmit data to the data lake platform. Data storage adopts distributed storage technology to ensure the safety and reliability of data. Data analysis uses machine learning, deep learning and other technologies to analyze data, Provide decision support.

Blockchain-Assisted Keyword Search Scheme for SWIM Service Based on Improved CSC-Cuckoo Filter

With the rapid development of the civil aviation industry, the demand for secure sharing of civil aviation data is increasing. As a global platform for secure sharing of civil aviation data, the security of the System Wide Information Management (SWIM) system is of great concern. SWIM adopts a service-oriented architecture and realizes the interaction of civil aviation data through a publish-subscribe model. To protect the service privacy while enabling subscribers to subscribe to the service quickly and securely, this paper proposes an efficient Circular Shift and Coalesce-Cuckoo Filter (CSC-CF)-based service Keyword Search (CCKS) scheme assisted by blockchain. This scheme uses the Symmetric-key Hidden Vector Encryption (SHVE) algorithm to encrypt and match service indexes and trapdoors to protect service privacy; the CSC-CF structure is applied to keyword search, which can effectively improve the efficiency of SWIM user subscription service. To improve search accuracy and space utilization, we propose the improved CCKS (ICCKS) scheme by optimizing the query algorithm of CSC-CF and setting a threshold for the number of keyword query failures. ICCKS improves search accuracy by 5–20% compared to CCKS when the filter’s space utilization is between 60 and 90%. Additionally, the scheme stores service topic indexes and registry on the blockchain and implements a smart contract to match indexes and subscription trapdoors, ensuring the integrity and trustworthiness of service topics and registry. Security analysis and experimental simulations demonstrate that the scheme is effective and secure in the SWIM system.