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Internet of Things (IoT) enables different smart environment objects to communicate without involving humans. Recently, IoT has started a new challenge within the healthcare sector called the Internet of Medical Things (IoMT). The huge amounts of data generated by IoMT entities need to be analyzed in real-time to improve the performance and quality of service of the IoMT applications. Mobile Edge Computing-enabled 5G system is shown as a successful paradigm to address such an obstacle. Numerous frameworks are introduced in literature based on this idea. This paper presents a thorough discussion of MEC-based IoMT healthcare systems.

The study examines the design of an information and educational System for quality education management. The requirements for the information environment of universities are formulated. The delineation of distinct stages in the technological process for shaping the values of information objects is outlined, achieved through the execution of information procedures. These procedures generate a technologically comprehensive product applicable within the university context. A set of heterogeneous systems for automating university activities makes obtaining integral characteristics based on information from different sources difficult. The research suggests an approach that allows you to get rid of the disadvantage. The introduction of an integrated information environment has been reviewed. To implement the information system “quality Management of Higher and Postgraduate Education,” a conceptual model of the system architecture and a logical structure was developed. The developed information model was based on the model of a complex information and educational environment of a higher educational institution and the Ontological model of the database of the integrated information and educational environment. The preliminary results of the pilot implementation of the system in the activities of Astana IT University are summarized, which made it possible to improve the quality management of the educational process.

Enterprise architecture (EA) is a discipline that is becoming increasingly important for businesses that are undergoing digital transformation. EA encompasses the practice of designing and managing the entire architecture of an organization, including its business processes, information systems, and technology infrastructure. In this paper, we will explore the main objectives of EA in digital transformation and the ways in which EA can help businesses achieve their digital transformation goals. Enterprise architects can use MicroServices technology to enable digital transformation by designing the MicroServices architecture, establishing best practices and standards, supporting tools such as DevOps, aligning with the enterprise's cloud strategy, and enabling data integration. Decision makers can use enterprise architecture with MicroServices to make digital transformation by leveraging the benefits of MicroServices architecture to achieve their digital transformation goals. [ABSTRACT FROM AUTHOR]

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances. [ABSTRACT FROM AUTHOR]

Cloud applications developed using a microservices approach require a scaling policy that adapts dynamically to the changing resource demand of the microservice while satisfying the application's quality of service (QoS) agreement. Current cloud technologies, including Kubernetes Horizontal Pod Autoscaler (HPA), autoscale microservices based on predefined autoscaling metrics, which require a very good knowledge of the application's resource usage. There is no built-in QoS autoscaling mechanism. In this work, we introduce an intelligent autoscaling module for the horizontal autoscaling of microservices in the cloud. We develop, train, validate, and deploy reinforcement learning (RL) agents on a real-time application using response time as a QoS. The agents, deployed on Google Cloud, can learn the microservices autoscaling resource usage metrics from the cloud environment and customize the Kubernetes HPA while minimizing response time. Our results show a decrease in response time compared to the default Kubernetes HPA. The agents provide an extendible autoscaling plug-in module that can adapt to new QoS metrics. [ABSTRACT FROM AUTHOR]

GraphQL is a query language and execution engine for web application programming interfaces (APIs) proposed as an alternative to improve data access problems and versioning of representational state transfer APIs. In this article, we thoroughly study the GraphQL field, first describing the GraphQL paradigm and its conceptual framework, and then conducting a systematic mapping study of 84 primary studies selected from an original set of 3,185. Our work analyzes trends or knowledge gaps about GraphQL by general classification of the studies and specific classification of this research topic. The study’s main conclusions show that GraphQL adoption is growing in the community as a strong alternative to implement APIs. However, we identified the need to strengthen the amount and rigor of empirical evidence collection in applied industry and government studies. In addition, we revealed the opportunity for specific studies on most GraphQL components, especially the consumption of GraphQL API services. [ABSTRACT FROM AUTHOR]

Microservices architecture has become known for its scalability and flexibility in recent years. Containerization of microservices using technologies such as Docker and Kubernetes has increased the efficiency of application deployment. However, this technological change has raised questions about the impact of containerization on security. This paper discusses various security aspects in the context of microservices containerization, including security, reliability, and data integrity. We explore challenges, best practices, and emerging trends affecting the security of containerized microservices. [ABSTRACT FROM AUTHOR]

The microservices architecture (MSA) style has been gaining interest in recent years because of its high scalability, ability to be deployed in the cloud, and suitability for DevOps practices. While new applications can adopt MSA from their inception, many legacy monolithic systems must be migrated to an MSA to benefit from the advantages of this architectural style. To support the migration process, we propose MicroMiner, a microservices identification approach that is based on static‐relationship analyses between code elements as well as semantic analyses of the source code. Our approach relies on machine learning (ML) techniques and uses service types to guide the identification of microservices from legacy monolithic systems. We evaluate the efficiency of our approach on four systems and compare our results to ground‐truths and to those of two state‐of‐the‐art approaches. We perform a qualitative evaluation of the resulted microservices by analyzing the business capabilities of the identified microservices. Also a quantitative analysis using the state‐of‐the‐art metrics on independence of functionality and modularity of services was conducted. Our results show the effectiveness of our approach to automate one of the most time‐consuming steps in the migration of legacy systems to microservices. The proposed approach identifies architecturally significant microservices with a 68.15% precision and 77% recall. [ABSTRACT FROM AUTHOR]

We propose a microservices-based framework for scalable data analysis in agriculture with IoT integration, leveraging the flexibility and modularity of microservices architecture to build a highly adaptable, maintainable, and efficient data analysis system. This framework allows for faster data processing and carry a diversity of agricultural data analysis tasks while maintaining scalability and fault tolerance. Despite the potential benefits, several challenges and obstacles need to be addressed, such as data integration and standardization, the development of agricultural-specific analytical microservices, and ensuring data security and privacy. Practical application and real-world validation are required to assess the impact of the proposed framework on the agricultural sector and inform future research directions. [ABSTRACT FROM AUTHOR]

With the rapid advancement of cloud-native computing, the microservice with high concurrency and low coupling has ushered in an unprecedented period of vigorous development. However, due to the mutability and complexity of cooperation procedures, it is difficult to realize high-efficient security management on these microservices. Traditional centralized access control has the defects of relying on a centralized cloud manager and a single point of failure. Meanwhile, decentralized mechanisms are defective by inconsistent policies defined by different participants. This paper first proposes a blockchain-based distributed access control policies and scheme, especially for microservices cooperation with dynamic access policies. We store the authorized security policies on the blockchain to solve the inconsistent policy problem while enabling individual management of personalized access policies by the providers rather than a central authority. Then we propose a graph-based decision-making scheme to achieve an efficient access control for microservices cooperation. Through the evaluations and experiments, it shows that our solution can realize effective distributed access control at an affordable cost. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary With the increasing popularity of cloud native and DevOps, container technology has become widely used in combination with microservices. However, deploying container‐based microservices in distributed edge‐cloud infrastructure requires complex selection strategies to ensure high‐quality service for users. Existing container orchestration tools lack flexibility in selecting the best deployment location based on user cost budgets and are insufficient in providing personalized deployment solutions. This paper proposes a genetic algorithm‐based Internet of Things (IoT) application deployment and selection strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints, which is an NP‐hard problem. The genetic algorithm is introduced to solve this problem effectively and improve deployment efficiency. Comparative results show that the proposed algorithm outperforms four baseline algorithms, including time‐greedy, cost‐greedy, random, and PSO, using real datasets and some synthetic datasets. The proposed algorithm provides personalized deployment solutions for edge‐cloud infrastructure. [ABSTRACT FROM AUTHOR]

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems have been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements (i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis. [ABSTRACT FROM AUTHOR]

The paper develops a structural model for building a system for developing methodological competence. The structural model is built based on a service-oriented approach to developing large software complexes and includes six subsystems. Each subsystem is implemented as a separate microservice, which allows system scaling. The paper describes a technique that allows you to build a teacher’s competency map and evaluate its eight components: cognitive, didactic, project, informational, communication, reflective, monitoring, and personal-motivational. A four-level scale is proposed for assessing the level of competencies. A methodology for evaluating the effectiveness of the methodical competence development system based on the hierarchical expert method has been developed. The system was verified and implemented. According to the results of the system implementation, the intensification of the educational process and the improvement of the training quality of students were recorded. Students’ success in studying subjects has increased significantly. Namely, the number of bad students has decreased by 15 %, the number of «excellent» students has increased by 10 %, and the number of «good» students has increased by 18 %. The paper touches upon creating information technologies adequate for improving the higher education system. The goal is the targeted development and implementation of information technologies in educational institutions and the creation and operation of integrated, flexible software to support a mixed system for organizing the educational process, as well as increasing the efficiency of implementing a mixed system for organizing the educational process. [ABSTRACT FROM AUTHOR]

Service choreographies are a versatile approach for building service-based distributed systems. Many approaches can be found in the literature tackling different aspects of service choreographies, such as choreography realizability and conformance checking, distributed coordination, formal choreographic languages, and scalability. As of today, choreography scalability has not been specifically addressed through approaches that also consider coordination issues while still decoupling these two related aspects. Scalability is one of the most important properties to be considered when building distributed systems. It enhances the user-perceived performances and influences the overall dependability of the system. In particular, load scalability allows distributed service-oriented systems to effectively handle varying loads without suffering performance degradation. In this direction, microservice-based systems are able to scale thanks to the possibility of replicating those microservices exposed to growing loads, distributing their workload among different instances. By leveraging on our experience in coordinating service choreographies, in this paper, we propose a layered architectural style that allows to realize scalable microservice-oriented choreographies. The architecture integrates a fully-distributed coordination layer capable of ensuring the correct interactions and a load-balancing layer that allows to balance of coordinated requests. We discuss the properties of the proposed architectural style and evaluate its benefits on user-perceived performances. [ABSTRACT FROM AUTHOR]

Microservices have emerged as a prevalent architectural style in modern software development, replacing traditional monolithic architectures. The decomposition of business functionality into distributed microservices offers numerous benefits, but introduces increased complexity to the overall application. Consequently, the complexity of authorization in microservice-based applications necessitates a comprehensive approach that integrates authorization as an inherent component from the beginning. This paper presents a systematic approach for achieving fine-grained user authorization using Attribute-Based Access Control (ABAC). The proposed approach emphasizes structure preservation, facilitating traceability throughout the various phases of application development. As a result, authorization artifacts can be traced seamlessly from the initial analysis phase to the subsequent implementation phase. One significant contribution is the development of a language to formulate natural language authorization requirements and policies. These natural language authorization policies can subsequently be implemented using the policy language Rego. By leveraging the analysis of software artifacts, the proposed approach enables the creation of comprehensive and tailored authorization policies. [ABSTRACT FROM AUTHOR]

The microservices architectural style has gained widespread acceptance. However, designing applications according to this style is still challenging. Common difficulties concern finding clear boundaries that guide decomposition while ensuring performance and scalability. With the aim of providing software architects and engineers with a systematic methodology, we introduce a novel actor-driven decomposition strategy to complement the domain-driven design and overcome some of its limitations by reaching a finer modularization yet enforcing performance and scalability improvements. The methodology uses a multi-level scalability assessment framework that supports decision-making over iterative steps. At each iteration, architecture alternatives are quantitatively evaluated at multiple granularity levels. The assessment helps architects to understand the extent to which architecture alternatives increase or decrease performance and scalability. We applied the methodology to drive further decomposition of the core microservices of a real data-intensive smart mobility application and an existing open-source benchmark in the e-commerce domain. The results of an in-depth evaluation show that the approach can effectively support engineers in (i) decomposing monoliths or coarse-grained microservices into more scalable microservices and (ii) comparing among alternative architectures to guide decision-making for their deployment in modern infrastructures that orchestrate lightweight virtualized execution units. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Locus: Penelitian dan Pengabdian is the property of Riviera Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The panorama of data is ever evolving, and big data has emerged to become one of the most hyped terms in the industry. Today, users are the perpetual producers of data that if gleaned and crunched, have the potential to reveal game-changing patterns. This has introduced an important shift regarding the role of data in organizations and many strive to harness to power of this new material. Howbeit, institutionalizing data is not an easy task and requires the absorption of a great deal of complexity. According to the literature, it is estimated that only 13% of organizations succeeded in delivering on their data strategy. Among the root challenges, big data system development and data architecture are prominent. To this end, this study aims to facilitate data architecture and big data system development by applying well-established patterns of microservices architecture to big data systems. This objective is achieved by two systematic literature reviews, and infusion of results through thematic synthesis. The result of this work is a series of theories that explicates how microservices patterns could be useful for big data systems. These theories are then validated through expert opinion gathering with 7 experts from the industry. The findings emerged from this study indicates that big data architectures can benefit from many principles and patterns of microservices architecture.

With software service transactions shifting from pay-before-use to pay-as-you-go, the Software as a Service (SaaS) subscription model is facing legalization and financialization challenges. This means that it does not accept financial payment on a pay-as-you-go basis, nor does it legally regulate the rights and obligations of service providers, consumers, and platforms. To address these issues, this paper introduces a new architecture called Smart Legal Contract (SLC), which is integrated into a service computing platform (SaaSC). To begin with, a contract-type service interface scheme is intended to handle the subscription process of service registration and publication on SaaS. In this scheme, we define six types of interactions, four kinds of microservice states, and their state transition procedures, and then establish the mapping from the general service interface following the OpenAPI Specification to the contract terms in the SLC-style SPESC language. To achieve a regularized interaction approach during service registration, a new term, called Service Registration Term (SRT), is proposed. Furthermore, the legal Negotiation-Acceptance mechanism is used to grant consumer rights to obtain software services. Second, in the process of service discovery and consumption, a payment mechanism for contracting demand is proposed. Specifically, based on the service matching approach with a three-level cache, other new terms, called Service Discovery Term (SDT) and Service Customization Term (SCT), are designed to specify the requests and responses of service discovery and invocation. A billing model driven by SRT, SDT, and SCT has been developed to implement fine-grained charging on the level of service interface calls and to evidence the preservation of service transactions in the blockchain. As a result, it provides a legal guarantee for the use of pay-as-you-go mode. From the aspect of service legalization, the SaaS+SaaSC architecture supports establishing three kinds of terms, including service registration, discovery, and customization terms, in an SLC-based software subscription contract so that a complete transaction procedure can be regulated among the three above parties based on their interactions, service states, and their transition process. In terms of service financialization, the interface declaration is appended to the SLC-based contract. By automatically executing smart contracts and checking the terms, the pay-as-you-go mode is implemented through fine-grained charging every time when calling the service interface. Furthermore, we take the weather forecast service as an example to implement and analyze the acquisition, delivery, and contractual payment of software services on blockchain smart contracts. The experimental results demonstrate the feasibility and effectiveness of the proposed SaaS+SaaSC architecture, which should be a practicable approach for contracting of software services.

Abstract Microservices architecture is widely used because of the ease of maintaining its microservices, as opposed to encapsulating functionality in a monolithic, which may negatively impact the development process when the application continues to grow. The migration process from a monolithic architecture to microservices became necessary, but it often relies on the architect's intuition only, which may cost many resources. A method to assist developers in decomposing monolithic into microservices is proposed to address that problem. Unlike the existing methods that often rely on non-source code artifacts which may lead into inaccurate decomposition if the artifacts do not reflect the latest condition of the monolith, the proposed method relies on analyzing the application source code to produce a grouping recommendation for building microservices. By using specific keyword extraction followed by Breadth First Search traversal with certain rules, the proposed method decomposed the monolith's component into several cluster whose majority of cluster’s members have uniform business domain. Based on the experiment, the proposed method got an 0.81 accuracy mean in grouping monolithic's components with similar business domain, higher than the existing decomposition method's score. Further research is recommended to be done to increase the availability of the proposed method.

The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution.

Structured Query Language (SQL) injections pose a constant threat to web services, highlighting the need for efficient detection to address this vulnerability. This study compares machine learning algorithms for detecting SQL injections in web microservices trained using a public dataset of 22,764 records. Additionally, a software architecture based on the microservices approach was implemented, in which trained models and the web application were deployed to validate requests and detect attacks. A literature review was conducted to identify types of SQL injections and machine learning algorithms. The results of random forest, decision tree, and support vector machine were compared for detecting SQL injections. The findings show that random forest outperforms with a precision and accuracy of 99%, a recall of 97%, and an F1 score of 98%. In contrast, decision tree achieved a precision of 92%, a recall of 86%, and an F1 score of 97%. Support Vector Machine (SVM) presented an accuracy, precision, and F1 score of 98%, with a recall of 97%.

Time-Sensitive Networking (TSN) aims to provide deterministic communications over Ethernet. The main characteristics of TSN are bounded latency and very high reliability, thus complying with the strict requirements of industrial communications or automotive applications, to name a couple of examples. In order to achieve this goal, TSN defines several scheduling algorithms, among them the Time-Aware Shaper (TAS), which is based on time slots and Gate Control Lists (GCLs). The configuration of network elements to allocate time slots, paths, and GCLs is laborious, and has to be updated promptly and in a dynamic way, as new data flows arrive or disappear. The IEEE 802.1Qcc standard provides the basis to design a TSN control plane to face these challenges, following the Software-Defined Networking (SDN) paradigm. However, most of the current SDN/TSN control plane solutions are monolithic applications designed to run on dedicated servers, and do not provide the required flexibility to escalate when facing increasing service requests. This work presents μTSN-CP, an SDN/TSN microservices-based control plane, based on the 802.1Qcc standard. Our architecture leverages the advantages of microservices, enabling the control plane to scale up or down in response to varying workloads dynamically. We achieve enhanced flexibility and resilience by breaking down the control plane into smaller, independent microservices. The performance of μTSN-CP is evaluated in a real environment with TSN switches, and various integer linear problem solvers, running over different computing platforms.

The cloudification and virtualisation of railway functions have the potential to improve railway operation efficiency, reliability, safety, and security, as well as to enhance passenger experience by offering innovative services. This paper considers issues related to the management and orchestration of railway clouds that host cloudified railway functions. A microservices-based approach to the design of railway cloud management and orchestration functionality is proposed. The basic railway cloud concepts were defined, and functionality related to the basic orchestration of the railway cloud and deployments is analysed in order to derive the requirements of platform resources and workload management. This functionality is further designed in the form of microservices, meaning that they could possibly be used in orchestration applications to enable improvements in scalability, fault isolation, and data security. The design of microservices follows the principles of the Representational State of Transfer (REST) application programming interface (API) as a set of interlinked resources. Resources related to railway cloud orchestration are identified with their associated data, relationships to other resources, and applicable methods. The resources’ methods are used in railway applications to implement the required orchestration functionality and to maintain the state of railway cloud orchestration processes. To verify the synthesised microservices, the common orchestration application logic and microservices’ logic were modelled, and it was proved that the orchestration processes, which run concurrently, expose equivalent behaviour. The proposed approach was validated using a simulation, aiming to evaluate injected latency as a key performance indicator for the reliability and safety of railway operations. Additionally, some safety and security issues related to railway cloud management and orchestration are considered.

Summary: There is a growing need for next‐generation science gateways to increase the accessibility of emerging large‐scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID‐19‐related challenges. Such science gateways that enable access to distributed computing resources for large‐scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio‐technical approach for developing a next‐generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID‐19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence‐based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug‐in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot‐assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. [ABSTRACT FROM AUTHOR]

Science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on‐demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e‐learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection, and deployment of cloud‐based reference architectures that can be automatically instantiated and executed even by nontechnical users. The framework also incorporates a knowledge repository exchange and learning module that provides embedded e‐learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere, and thermosphere research communities. [ABSTRACT FROM AUTHOR]

In the past few years the use of IoT devices has grown exponentially. When it comes to working with them, we find a series of problems that are not easy to solve. On the one hand, the simple fact of communicating with those devices can be problematic since they can use different types of technologies regarding that communication. On the other hand, these types of devices usually aim to perform their function using the lowest possible energy, meaning they have certain constraints in terms of performance. Trying to work with these devices in high-availability environments becomes difficult because of those restraints. This paper introduces digital dice, a virtual representation of IoT devices and cyber-physical systems based on microservices that uses the standard established by the W3C, the Web of Things, as the underlying framework to declare its possible interactions. The article puts forward the different strategies that Digital Dice uses to mitigate the problems raised. Furthermore, we contrast the performance of Digital Dice with using the devices directly, demonstrating its advantages in a process that requires High Availability. [ABSTRACT FROM AUTHOR]

The symmetry between customer expectations and operator goals, on one hand, and the digital transition of the railways, on the other hand, is one of the main factors affecting green transport sustainability. The European Train Control System (ETCS) was created to improve interoperability between different railway signaling systems and increase safety and security. While there are a lot of ETCS Level 2 deployments all over the world, the specifications of ETCS Level 3 are under development. ETCS Level 3 is expected to have a significant impact on automatic train operation, protection, and supervision. In this paper, we present an innovative control system architecture that allows the incorporation of artificial intelligence (AI)/machine learning (ML) applications. The architecture features control function virtualization and programmability. The concept of an intelligent railway controller (IRC) is introduced as being a piece of cloud software responsible for the control and optimization of railway operations. A microservices-based approach to designing the IRC's functionality is presented. The approach was formally verified, and some of its performance metrics were identified. [ABSTRACT FROM AUTHOR]

As medical services increasingly trying to harness advances in connected medical devices and the use of artificial intelligence, a new modeling strategy is essentially required to enable developers of the emerging medical applications to organize, integrate, and retain information in this new era of service-oriented healthcare. Such strategy needs to adhere to the principle of knowledge coupling that was advocated by Lawrence Weed, the father of modern problem-oriented medical records, in early 1970s where he defined the way medical information should be described for higher decision making. There has never been a more compelling time to use knowledge coupling related to both medical knowledge and the services build around them. The new digital technologies such as microservices, graph-based databases, Internet of Healthcare Things, and Cloud Computing as well as the non-digital disruptive events such as the pandemic have accelerated the adoption of new notions of service integration and knowledge coupling to provide the long waited solution for interoperability in healthcare as well as higher level of knowledge integration and analytics. The cornerstone of every new change is pointing toward the use of microservices and knowledge graph APIs to be able to thrive and lead in the uncertainty and healthcare change. This research paper uses the notion of mindmap to push conversation and guide scholars in developing effective microservice-based care systems that utilize knowledge graphs and the new care standards including the HL7 FHIR. Central to our mindmap is the GraphQL graph-based technology and the medical diagnosis as a service. This mindmap is the starting research point of our MITACS 2021 and NSERC DDG 2021 projects. [ABSTRACT FROM AUTHOR]

Microservices have become promising candidates for the deployment of network and vertical functions in the fifth generation of mobile networks. However, microservice platforms like Kubernetes use a flat networking approach towards the connectivity of virtualised workloads, which prevents the deployment of network functions on isolated network segments (for example, the components of an IP Telephony system or a content distribution network). This paper presents L2S-M, a solution that enables the connectivity of Kubernetes microservices over isolated link-layer virtual networks, regardless of the compute nodes where workloads are actually deployed. L2S-M uses software-defined networking (SDN) to fulfil this purpose. Furthermore, the L2S-M design is flexible to support the connectivity of Kubernetes workloads across different Kubernetes clusters. We validate the functional behaviour of our solution in a moderately complex Smart Campus scenario, where L2S-M is used to deploy a content distribution network, showing its potential for the deployment of network services in distributed and heterogeneous environments. [ABSTRACT FROM AUTHOR]

Automatic identification of microservices in legacy web apps has attracted considerable attention, leading to the proposal of various approaches to address this problem. Some of the proposed approaches focus on the words that make up web apps. This paper proposes a novel approach that utilizes the context in which words appear in web components. To this end, a neighbor relationship between words is defined and extracted in terms of the hierarchical structure of a web component. Then web components are embedded using the neighbor relationships and classified into appropriate microservices. Experiments are conducted using open-source web apps to validate the proposed approach. [ABSTRACT FROM AUTHOR]

Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems. [ABSTRACT FROM AUTHOR]

Information Technology involves solutions for many kinds of industries and organizations, offering conditions for solving problems of different types and complexities. Artificial Intelligence, and more specifically applications that considers Machine Learning (ML) and Software Technology are part of these solutions for solving problems, including solutions for solving problems that involve smart cities approach. In order to present frameworks that deal with the operationalization of Machine Learning and Software technology, this article is based on the study and evaluation of frameworks that involve Machine Learning Operations (MLOps) and microservices. Specifically, three frameworks that integrate ML algorithms with microservices are evaluated based on a bibliographical review in scientific journals of relevance to the area. From an exploratory analysis of these frameworks, it was possible to highlight their main objectives, their benefits, and their ability to offer solutions that favor the large-scale use of Machine Learning algorithms in problem solving. The main results are highlighted in the article through a qualitative analysis that considers six evaluation criteria, such as: capacity for sharing resources, scope of use by users, and use in a cloud environment. The results achieved are satisfactory since the work allows, through a qualitative view of the evaluated frameworks, a perspective of how the integration of MLOps and microservices has been carried out, its benefits and possible results achieved through this integration. [ABSTRACT FROM AUTHOR]

COVID‐19 has exposed and widened the disparity in education. The paper reviews the efforts made by educational institutions and organizations to offer services to the disadvantaged in an effort to eliminate the educational gap. Based on the literature, the primary issues which led to increasing in the gap of educational inequality were identified as the need for books, internet connection, study gadgets/devices, extra tutoring, food and study space, and psychological counseling. This paper proposes an online system using the microservices architecture to provide a holistic system that addresses the key concerns that contributed to the educational discrepancy. The application proposes a variety of business services that are logically separated from each other, and that can be deployed and scaled independently. Each of the provided services is autonomous and can communicate with other services. [ABSTRACT FROM AUTHOR]