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The digital twins process of equipment operation and maintenance involves massive data and complex business logic. Through data awareness of equipment and workshop environment. According to the characteristics of equipment operation and maintenance, the data characteristics and requirements are analysed, an equipment operation and maintenance management system based on the micro-service framework is proposed, a set of equipment operation and maintenance digital twins are designed and a general reference software architecture is designed and by checking the underlying in-heap pattern reduces high-impact portions of memory to provide accurate follow-up complex digital twins modelling provides the architectural foundation. [ABSTRACT FROM AUTHOR]

Lead generation refers to the identification of potential topics (the 'leads') of importance for journalists to report on. In this article we present μ XL, a new lead generation tool based on a microservice architecture that includes a component of explainable AI. 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. [ABSTRACT FROM AUTHOR]

Microservice architecture splits the traditional monolithic application into different small services. Differences in programming language and data structure make communication between each service difficult, and the communication performance between services directly affects the performance of the entire microservice architecture. Thus, communication performance improvement between services has become a challenge for microservice architectures. This study proposes a microservice service communication technique called remote procedure call multiple (RPCM), which uses different network communication models to achieve inter-service communication based on the domain in which the services are located. RPCM can be used to improve the communication performance between services. We conducted performance stress comparison experiments between RPCM and two other service communication technologies. We evaluated RPCM's performance by measuring the time spent processing requests and transaction performance stress metrics, such as transactions per second, using different threads and numbers of requests in both local and remote domains. The extensive experimental results showed that RPCM performs significantly better than the other techniques under local domain conditions. Furthermore, RPCM helps deploy different services based on the performance requirements to achieve the best microservice communication performance. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA's utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems. [ABSTRACT FROM AUTHOR]

The capability to integrate Internet of Things (IoT) technologies into business processes (BPs) has emerged as a transformative paradigm, offering unprecedented opportunities for organisations to enhance their operational efficiency and productivity. Interacting with the physical world and leveraging real-world data to make more informed business decisions is of greatest interest, and the idea of IoT-enhanced BPs promises to automate and improve business activities and permit them to adapt to the physical environment of execution. Nonetheless, combining these two domains is challenging, and it requires new modelling methods that do not increase notation complexity and provide independent execution between the process and the underlying device technology. In this work, we propose FloBP, a model-driven engineering approach separating concerns between the IoT and BPs, providing a structured and systematic approach to modelling and executing IoT-enhanced BPs. Applying the separation of concerns through an interdisciplinary team is needed to ensure that the approach covers all necessary process aspects, including technological and modelling ones. The FloBP approach is based on modelling tools and a microservices architecture to deploy BPMN models, and it facilitates integration with the physical world, providing flexibility to support multiple IoT device technologies and their evolution. A smart canteen scenario describes and evaluates the approach's feasibility and its possible adoption by various stakeholders. The performed evaluation concludes that the application of FloBP facilitates the modelling and development of IoT-enhanced BPs by sharing and reusing knowledge among IoT and BP experts. [ABSTRACT FROM AUTHOR]

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches. [ABSTRACT FROM AUTHOR]

Edge devices generate a lot of data, and the data is received and utilized according to each service cycle of applications. It is difficult to handle the amount of data in existing cloud environments. MEC environments can reduce network latency and eliminate performance bottlenecks so that attempts have been made to run DL services on various heterogeneous devices. However, due to limited computing resources, inference may fail to work or may be time-consuming. This paper proposes a MEC-based AI computing partitioning model that enables distributed execution of AI applications on heterogeneous edge devices. The suggested model allows users to determine the number of divisions of AI network models, and has partitioned models with uniform parameters. According to the experimental results, as the compute partitioning increases, the edge device's overhead decreases on average by 25.8%, 14.3%, and 3.27% in terms of execution time, CPU usage, and memory usage, respectively, making it possible to provide seamless AI application services through distributed execution. [ABSTRACT FROM AUTHOR]

The calibration industry is renowned for its diverse and sophisticated equipment and complex processes, which necessitate innovative solutions to keep pace with rapidly advancing technology. This paper introduces an enhancement to an existing microservice-based cloud architecture, aimed at effectively managing the inherent complexity within this field. The enhanced architecture seamlessly integrates various equipment types and communication technologies, aligning diverse stakeholder expectations into a unified system that ensures efficient and accurate calibration processes. It highlights the integration of microservices to facilitate various methods of uncertainty calculation and the generation of digital calibration certificates (DCCs). A case study on RF power measurement illustrates the practical application and benefits of the enhanced architecture. Although initially focused on RF power measurement, the flexible architecture allows for future expansions to accommodate new standards and measurement techniques. The enhanced system offers a comprehensive approach to managing data flow from calibration equipment to the final generation of DCCs, utilizing cloud-based services for efficient data processing. As a future direction, this extension sets the groundwork for broader applicability across multiple measurement types, ensuring readiness for upcoming advancements in metrology. [ABSTRACT FROM AUTHOR]

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application's performance. This aspect, in particular, can significantly affect the QoS manager's ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system's current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm. [ABSTRACT FROM AUTHOR]

Container security has received much research attention recently. Previous work has proposed to apply various machine learning techniques to detect security attacks in containerized applications. On one hand, supervised machine learning schemes require sufficient labeled training data to achieve good attack detection accuracy. On the other hand, unsupervised machine learning methods are more practical by avoiding training data labeling requirements, but they often suffer from high false alarm rates. In this article, we present a generic self-supervised hybrid learning (SHIL) framework for achieving efficient online security attack detection in containerized systems. SHIL can effectively combine both unsupervised and supervised learning algorithms but does not require any manual data labeling. We have implemented a prototype of SHIL and conducted experiments over 46 real-world security attacks in 29 commonly used server applications. Our experimental results show that SHIL can reduce false alarms by 33%–93% compared to existing supervised, unsupervised, or semi-supervised machine learning schemes while achieving a higher or similar detection rate. [ABSTRACT FROM AUTHOR]

Co-locating latency-critical services (LCSs) and best-effort jobs (BEJs) constitute the principal approach for enhancing resource utilization in production clusters. Nevertheless, the co-location practice hurts the performance of LCSs due to resource competition, even when employing isolation technology. Through an extensive analysis of voluminous real trace data derived from two production clusters, we observe that BEJs typically exhibit periodic execution patterns and serve as the primary sources of interference to LCSs. Furthermore, despite occupying the same level of resource consumption, the diverse compositions of BEJs can result in varying degrees of interference on LCSs. Subsequently, we propose PISM, a proactive Performance Interference Scoring and Mitigating framework for LCSs through the optimization of BEJ scheduling. Firstly, PISM adopts a data-driven approach to establish a characterization and classification methodology for BEJs. Secondly, PISM models the relationship between the composition of BEJs on servers and the response time (RT) of LCSs. Thirdly, PISM establishes an interference scoring mechanism in terms of RT, which serves as the foundation for BEJ scheduling. We assess the effectiveness of PISM on a small-scale cluster and through extensive data-driven simulations. The experiment results demonstrate that PISM can reduce cluster interference by up to 41.5%, and improve the throughput of long-tail LCSs by 76.4%. [ABSTRACT FROM AUTHOR]

As container technology and microservices mature, applications increasingly shift to microservices and cloud deployment. Growing microservices scale complicates resource scheduling. Traditional methods, based on fixed thresholds, are simple but lead to resource waste and poor adaptability to traffic spikes. To address this problem, we design a new resource scheduling strategy Saver based on the container cloud platform, which combines a microservice request prediction model with a microservice performance evaluation model that predicts SLO (Service Level Objective) violations and a heuristic algorithm to solve the optimal resource scheduling for the cluster. We deploy the microservices open-source project sock-shop in a Kubernetes cluster to evaluate Saver. Experimental results show that Saver saves 7.9% of CPU resources, 13% of the instances, and reduces the SLO violation rate by 31.2% compared to K8s autoscaler. [ABSTRACT FROM AUTHOR]

Privacy by design is nowadays recognized as essential in bringing data privacy into software systems. However, developers still face many challenges in reconciling privacy and software requirements and implementing privacy protections in software systems. One emerging trend is the adoption of microservices architectures—they bring in some qualities that can benefit privacy by design. The main goal of this study is to adapt privacy by design to the qualities brought by microservices. The main focus is at the architectural level, where the main structural decisions are made. A systematic literature review is adopted to identify a set of privacy models that underscore significant differences in software systems' protection using microservices. From the literature review, a decision framework is developed. The decision framework provides guidance and supports design decisions in implementing data privacy using microservices. The framework helps select and integrate different privacy models. An illustration of using the framework, which considers the design of an electronic voting system, is provided. This study contributes to closing the gap between regulation and implementation through design, where decisions related to data privacy are integrated with decisions on architecting systems using microservices. [ABSTRACT FROM AUTHOR]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [ABSTRACT FROM AUTHOR]

Computer system resilience refers to the ability of a computer system to continue functioning even in the face of unexpected events or disruptions. These disruptions can be caused by a variety of factors, such as hardware failures, software glitches, cyber attacks, or even natural disasters. Modern computational environments need applications that can recover quickly from major disruptions while also being environmentally sustainable. Balancing system resilience with energy efficiency is challenging, as efforts to improve one can harm the other. This paper presents a method to enhance disaster survivability in microservice architectures, particularly those using Kubernetes in cloud-based environments, focusing on optimizing electrical energy use. Aiming to save energy, our work adopt the consolidation strategy that means grouping multiple microservices on a single host. Our aproach uses a widely adopted analytical model, the Generalized Stochastic Petri Net (GSPN). GSPN are a powerful modeling technique that is widely used in various fields, including engineering, computer science, and operations research. One of the primary advantages of GSPN is its ability to model complex systems with a high degree of accuracy. Additionally, GSPN allows for the modeling of both logical and stochastic behavior, making it ideal for systems that involve a combination of both. Our GSPN models compute a number of metrics such as: recovery time, system availability, reliability, Mean Time to Failure, and the configuration of cloud-based microservices. We compared our approach against others focusing on survivability or efficiency. Our approach aligns with Recovery Time Objectives during sudden disasters and offers the fastest recovery, requiring 9% less warning time to fully recover in cases of disaster with alert when compared to strategies with similar electrical consumption. It also saves about 27% energy compared to low consolidation strategies and 5% against high consolidation under static conditions. [ABSTRACT FROM AUTHOR]

In the evolving landscape of software architecture, the shift from monolithic structures to agile, scalable microservices has revolutionized cloud-native application development. However, the inherent dynamism of microservices can lead to the inadvertent creation of unnecessary microservices, introducing complexity and inefficiency. Moreover, with a lack of control mechanisms in evolution, systems can lead to what is known as architecture degradation. This research ventures into the emerging domain of microservice-to-microservice transformation, a concept focused on optimizing existing cloud-native systems. We experiment with a machine learning methodology initially designed for monolith-to-microservices migration, adapting it to the complex microservices landscape, with a specific focus on the train-ticket application (Zhou in Association for Computing Machinery, https://doi.org/10.1145/3183440.3194991), which is an established system benchmark in the community. To identify the optimal microservice distribution, we employ a combination of the Variational Autoencoder and fuzzy c-means clustering. Our results demonstrate a close resemblance to the original application in terms of structural modularity. Though they fall short of achieving the ideal interface number exhibited by the original microservices, our findings highlight the potential of automated microservice composition, effectively narrowing the gap between human-designed and machine-generated microservices and advancing the field of software architecture. [ABSTRACT FROM AUTHOR]

Microservice architecture is the mainstream driver for cloud-native systems. It brings various benefits to the development process, such as enabling decentralized development and evolution of self-contained system parts, facilitating their selective scalability. However, new challenges emerge in such systems as the system-holistic quality assurance becomes difficult. It becomes hard to maintain the desired system architecture since many teams are involved in the development process and have greater autonomy. Without instruments and practices to coordinate teams and assess the system as a whole, the system is prone to architectural degradation. To face such challenges, various architectural aspects of the system should be accessible to practitioners. It would give them a better understanding of interconnections and dependencies among the microservice they manage and the context of the entire system. This manuscript provides the perspective on uncovering selected system architectural views using static code analysis. It demonstrates that holistic architectural views can be effectively derived from the system codebase(s), highlighting dependencies across microservices. Such new perspectives will aid practitioners in making informed decisions when intending to change and evolve the system. Moreover, with such a new instrument for system holistic assessment, we quickly realize that human experts must cope with another problem, the evergrowing scales of cloud-native systems. To elaborate on the topic, this manuscript examines how static analysis outcomes can be transformed into interactive architectural visualizations to assist practitioners in handling large-scale complexities. [ABSTRACT FROM AUTHOR]

Microservices are containerized, loosely coupled, interactive smaller units of the application that can be deployed, reused, and maintained independently. In a microservices-based application, allocating the right computing resources for each containerized microservice is important to meet the specific performance requirements while minimizing the infrastructure cost. Microservices-based applications are easy to scale automatically based on incoming workload and resource demand automatically. However, it is challenging to identify the right amount of resources for containers hosting microservices and then allocate them dynamically during the auto-scaling. Existing auto-scaling solutions for microservices focus on identifying the appropriate time and number of containers to be added/removed dynamically for an application. However, they do not address the issue of selecting the right amount of resources, such as CPU cores, for individual containers during each scaling event. This paper presents a novel approach to dynamically allocate the CPU resources to the containerized microservice during the autoscaling events. Our proposed approach is based on the machine learning method, which can identify the right amount of CPU resources for each container, dynamically spawning for the microservices over time to satisfy the application's response time requirements. The proposed solution is evaluated using a benchmark microservices-based application based on real-world workloads on the Kubernetes cluster. The experimental results show that the proposed solution outperforms by yielding a 40% to 60% reduction in violating the response time requirements with 0.5 × to 1.5 × less cost compared to the state-of-art baseline methods. [ABSTRACT FROM AUTHOR]

The dark web, often linked with illegal activities, can be monitored with different solutions. However, these tools are typically purpose-specific and designed for unique use cases. In this study, we propose a flexible and scalable framework that facilitates the easy integration of new workflows for dark web analysis. The design is based on the control, logic and operations layers, supplemented by a tools module, logs management, asynchronous message-based communication and a database. The implementation maps the features into a microservice approach, utilizing the open-source technologies Docker Swarm, Kafka, ELK Stack (Elastic Search, Logstash and Kibana), and PostgreSQL. A workflow to scrape web elements of Tor onion services is deployed and validated, demonstrating considerable framework performance despite the time-consuming task of navigating the dark web. Over 16 h, the framework collected over half million onion domains (84,371 unique ones) and made 78,555 accesses to them. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Yearly, the rates of Internet penetration are on the rise, surpassing 80% in developed nations. Despite this progress, over two billion individuals in rural and low-income regions face a complete absence of Internet access. This lack of connectivity hinders the implementation of vital services like remote healthcare, emergency assistance, distance learning, and personal communications. To bridge this gap and bring essential services to rural populations, this paper leverages Unmanned Aerial Vehicles (UAVs). The proposal introduces a UAV-based network architecture and an energy-efficient algorithm to deploy Internet of Things (IoT) applications. These applications are broken down into microservices, strategically distributed among a subset of UAVs. This approach addresses the limitations associated with running an entire IoT application on a single UAV, which could lead to suboptimal outcomes due to battery and computational constraints. Simulation results conducted in a realistic scenario underscore the effectiveness of the proposed solution. The evaluation includes assessing the percentage of IoT requests successfully served to users in the designated area and reducing the energy consumption required by UAVs during the handling of such requests. [ABSTRACT FROM AUTHOR]

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3 × to 9.5 × better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2 × to 19 × better sampling performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Microservices has become a buzzword in industry as many large IT giants such as Amazon, Twitter, Uber, etc have started migrating their existing applications to this new style and few of them have started building their new applications with this style. Due to increasing user requirements and the need to add more business functionalities to the existing applications, the web applications designed using the microservices style also face a few performance challenges. Though this style has been successfully adopted in the design of large enterprise applications, still the applications face performance related issues. It is clear from the literature that most of the articles focus only on the backend microservices. To the best of our knowledge, there has been no solution proposed considering micro frontends along with the backend microservices. To improve the performance of the microservices based web applications, in this paper, a new framework for the design of web applications with micro frontends for frontend and microservices in the backend of the application is presented. To assess the proposed framework, an empirical investigation is performed to analyze the performance and it is found that the applications designed with micro frontends with microservices have performed better than the applications with monolithic frontends. Additionally, to predict the performance of microservices based applications, a machine learning model is proposed as machine learning has wide applications in software engineering related activities. The accuracy of the proposed model using different metrics is also presented. [ABSTRACT FROM AUTHOR]

With the booming development of the electricity market, market factors such as electricity trading varieties are growing rapidly. The frequency of transactions has become increasingly real-time, and transaction clearing and settlement tasks have become more complex. The increasing demands for concurrent access and carrying capacity in trading systems have made it increasingly difficult for existing systems to support business. This article proposes a transaction support system for large-scale electricity trading market entities, which solves the problems of high concurrency access and massive access data calculation while ensuring system security through business isolation measures. The system uses microservices to treat various functional modules as independent service modules, thus making service segmentation and composition more flexible. By using read–write separation, caching mechanisms, and several data reliability assurance measures, data can be stored and accessed quickly and securely. The use of a three-layer load balancing module consisting of an OpenResty access entry layer, a gateway routing gateway layer, and a WebClient service inter-resource invocation layer can effectively improve the system's ability to handle concurrent access. [ABSTRACT FROM AUTHOR]

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Modern industrial automation supported by Cyber–Physical Systems (CPSs) requires high flexibility, which is achieved through increased interconnection between modules. This interconnection introduces a layer of symmetry into the design and operation of CPSs, balancing the distribution of tasks and resources across the system and streamlining the flow of information. However, this adaptability also exposes control systems to security threats, particularly through novel communication links that are vulnerable to cyberattacks. Traditional strategies may have limitations in these applications. This research proposes a design approach for control applications supported by CPSs that incorporates cyberattack detection and tolerance strategies. Using a modular and adaptive approach, the system is partitioned into microservices for scalability and resilience, allowing structural symmetry to be maintained. Schedulability assessments ensure that critical timing constraints are met, improving overall system symmetry and performance. Advanced cyberattack detection and isolation systems generate alarms and facilitate rapid response with replicas of affected components. These replicas enable the system to recover from and tolerate cyberattacks, maintaining uninterrupted operation and preserving the balanced structure of the system. In conclusion, the proposed approach addresses the security challenges in CPS-based control applications and provides an integrated and robust approach to protect industrial automation systems from cyber threats. A case study conducted at a juice production facility in Colima, México, demonstrated how the architecture can be applied to complex processes such as pH control, from simulation to industrial implementation. The study highlighted a plug-and-play approach, starting with component definitions and relationships, and extending to technology integration, thereby reinforcing symmetry and efficiency within the system. [ABSTRACT FROM AUTHOR]

The microservices architectural style aims at improving software maintenance and scalability by decomposing applications into independently deployable components. A common criticism about this style is the risk of increasing response times due to communication, especially with very granular entities. Locality‐aware placement of microservices onto the underlying hardware can contribute to keeping response times low. However, the complex graphs of invocations originating from users' calls largely depend on the specific workload (e.g., the length of an invocation chain could depend on the input parameters). Therefore, many existing approaches are not suitable for modern infrastructures where application components can be dynamically redeployed to take into account user expectations. This paper contributes to overcoming the limitations of static or off‐line techniques by presenting a big data pipeline to dynamically collect tracing data from running applications that are used to identify a given number k$$ k $$ of microservices groups whose deployment allows keeping low the response times of the most critical operations under a defined workload. The results, obtained in different working conditions and with different infrastructure configurations, are presented and discussed to draw the main considerations about the general problem of defining boundary, granularity, and optimal placement of microservices on the underlying execution environment. In particular, they show that knowing how a specific workload impacts the constituent microservices of an application, helps achieve better performance, by effectively lowering response time (e.g., up to a 61%$$ 61\% $$ reduction), through the exploitation of locality‐driven clustering strategies for deploying groups of services. [ABSTRACT FROM AUTHOR]

In the context of the Industrial Internet of things (IIoT), large-scale IIoT data is generated, which can be effectively mined to provide valuable information for condition monitoring (CM). However, traditional CM methods cannot meet unprecedented challenges concerning large-scale IIoT data transmission, storage and analysis. Therefore, manufacturers have begun to shift from the traditional manufacturing paradigm to smart manufacturing, which integrates the encapsulated manufacturing services and the enabling cloud-edge computing technology to handle large-scale IIoT data. To enhance the agility, scalability and portability of traditional manufacturing services, a microservices-based cloud-edge collaborative CM platform for smart manufacturing systems is proposed. First, leveraging the microservices management system, the lightweight edge and cloud services are constructed from the microservices level, which enables flexible deployment and upgrade of services. Next, the proposed platform architecture effectively integrates the computing and storage capabilities of the cloud layer and the real-time nature of the edge layer, where the cloud-edge collaborative mechanism is introduced to achieve real-time diagnosis and enhance prognosis accuracy. Finally, based on the proposed system, the diagnosis and prognosis tasks are implemented on a manufacturing line, and the results show that the diagnostic accuracy is 90% and the prediction error is 50%. [ABSTRACT FROM AUTHOR]

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other. [ABSTRACT FROM AUTHOR]

As the area of robotics achieves promising results, there is an increasing need to scale robotic software architectures towards real-world domains. Traditionally, robotic architectures are integrated using common frameworks, such as ROS. Therefore, systems with a uniform structure are produced, making it difficult to integrate third party contributions. Virtualisation technologies can simplify the problem, but their use is uncommon in robotics and general integration procedures are still missing. This paper proposes and evaluates a containerised approach for designing and integrating multiform robotic architectures. Our approach aims at augmenting preexisting architectures by including third party contributions. The integration complexity and computational performance of our approach is benchmarked on the EU H2020 SecondHands robotic architecture. Results demonstrate that our approach grants simplicity and flexibility of setup when compared to a non-virtualised version. The computational overhead of using our approach is negligible as resources were optimally exploited. [ABSTRACT FROM AUTHOR]

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster's hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system's ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As a new software design paradigm, microservices structure an application as a collection of services that are independently deployable and loosely coupled. A key step of migrating non‐microservices‐based systems to microservices‐based systems is the identification of microservices in the target application. Traditional approaches to identify microservices, however, usually suffer from lack of full automation and low effectiveness. This paper puts forward an expert system to identify microservices automatically from legacy systems by leveraging the similarity of RESTful APIs. The system consists of three major parts. The first part calculates the candidate topic similarity and the response message similarity of APIs, and the overall similarity is obtained through their combination. Afterwards, the second part constructs a graph of API similarities with API as the node and the overall similarity as the weight. The third part employs a graph‐based clustering algorithm to identify candidate microservices from the API similarity graph. Experiments conducted on open‐source projects demonstrate the effectiveness of our system. [ABSTRACT FROM AUTHOR]

In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users' ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP considers various factors such as response time, throughput, CPU usage, and other metrics to dynamically model interactions between microservice performance indicators for diagnosis and prediction. Using experimental data from our monitoring tool, stakeholders can build various networks for probabilistic analysis of microservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service (QoS) level. We generated a dataset of microservices with 2726 records across four benchmarks including CPU, memory, response time, and throughput to demonstrate the efficacy of the proposed MPDP architecture. We validate MPDP and demonstrate its capability to predict microservice performance. We compared various Bayesian networks such as the Noisy-OR Network (NOR), Naive Bayes Network (NBN), and Complex Bayesian Network (CBN), achieving an overall accuracy rate of 89.98% when using CBN. [ABSTRACT FROM AUTHOR]

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider's operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate. [ABSTRACT FROM AUTHOR]

In this paper, we conducted a state-of-the-art survey on the current state of IoT platforms suitable for the development of smart city (SC) applications. Both commercial and open-source IoT platforms are presented and compared, addressing various significant aspects and characteristics of SC applications, such as connectivity, communication protocols, dashboards/analytics availability, security, etc. The characteristics of all the investigated platforms were aggregated so that useful outcomes regarding the technological trends of the IoT platforms could be derived. Furthermore, an attempt was made to identify any discrepancies between the needs of smart cities and the capabilities provided by the relevant platforms. Moreover, IoT platforms referring to the domains of industry, agriculture, and asset tracking were also included, alongside platforms that purely target smart cities, as parts of them are also applicable to smart city applications. The results of the comparison proved that there is a lack of open-source IoT platforms targeted at smart cities, which impedes the development and testing of connected smart city applications for researchers. [ABSTRACT FROM AUTHOR]

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 μ TSN-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 μ TSN-CP is evaluated in a real environment with TSN switches, and various integer linear problem solvers, running over different computing platforms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Investigating digital crimes in cloud service environments is complex due to the decentralized nature of these services, posing challenges in data collection and presenting credible evidence in court. While existing research focuses more on external investigators, Cloud Service Providers (CSPs) have less responsibilities. To address this gap, a new framework named Microservices Forensics as a Service (MsFaaS) is introduced, aiming to ensure the reliable presentation of evidence. MsFaaS integrates international law enforcement, assigning responsibility to CSPs validated by local authorities where incidents occur. The framework consolidates existing literature, tackling unresolved challenges like legality, standardization, and data collection through the collection of diverse data types and the use of event reconstruction techniques to construct a comprehensive crime scene in both real-time and postmortem scenarios. Blockchain secures collected data against tampering, while hash functions and public key cryptography validate Microservices workflows against man-in-the-middle attacks. Machine learning enables proactive response actions to incidents. Moreover, MsFaaS facilitates auditing and recording of both internal and external cloud traffic, producing evidence reports certified by local authorities. By addressing the limitations of traditional digital forensics, MsFaaS enhances investigation reliability and effectiveness, offering services for internal CSP auditing and maintaining Chain of Custody integrity critical for trial decision-making. [ABSTRACT FROM AUTHOR]

As the most successful realization of serverless, function as a service (FaaS) brings in a novel cloud computing paradigm that can save operating costs, reduce management effort, enable seamless scalability, and augment development productivity. Migration of an existing application to the serverless architecture is, however, an intricate task as a great number of decisions need to be made along the way. We propose in this paper RADF, a semi‐automatic approach that decomposes a monolith into serverless functions by analyzing the business logic inherent in the interface of the application. The proposed approach adopts a two‐stage refactoring strategy, where a coarse‐grained decomposition is performed at first, followed by a fine‐grained one. As such, the decomposition process is simplified into smaller steps and adaptable to generate a solution at either microservice or function level. We have implemented RADF in a holistic DevOps methodology and evaluated its capability for microservice identification and feasibility for code refactoring. In the evaluation experiments, RADF achieves lower coupling and relatively balanced cohesion, compared to previous decomposition approaches. [ABSTRACT FROM AUTHOR]

Microservices is a structural approach, where multiple small set of services are composed and processed independently with lightweight communication mechanism. To accomplish the end‐user demand in minimum delay and cost without violating the service level agreement (SLA) constraints and overhead is a challenging issue in cloud computing. In addition, existing framework tries to deploy the microservice over the best computing resource for latency‐sensitive applications, but long boot‐time, and low resource utilization still remains a challenging task. To find the solution for aforementioned issues, we propose a Quality of Service (QoS) aware resource allocation model based on a Fine‐tuned Sunflower Whale Optimization Algorithm (FSWOA) that find the best resources for microservice deployment and fulfill the objectives of users as well as service provider. The proposed technique deploys the container‐based services over the physical machine based upon the capacity, to execute the micro services by utilizing the CPU and memory maximally. The proposed work aims is to distribute the workload in efficient manner and avoid the wastage of resources that leads to optimize the QoS parameters. The experimental results conducted in simulation environment demonstrates that proposed approach perform superior over baseline approaches and reduces the time, memory consumption, CPU consumption, and service cost up to 4.26%, 11.29%, 17.07% and 24.22% compared to SFWAO, GA, PSO and ACO. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making. [ABSTRACT FROM AUTHOR]

In addressing the inefficiencies and limitations in proactive defense against Regular Expression Denial of Service (ReDoS) attacks in cloud-native environments, we have developed a defense method based on Moving Target Defense (MTD) technology. Initially, we analyzed the behaviors of both attackers and defenders within microservice applications characteristic of cloud-native environments. Subsequently, leveraging Kubernetes, we de signed an MTD-based defense system. This system incorporates dynamic and static multi-dimensional microservice weight indices based on topology information and request arrival rates, as well as service efficiency judgment indices based on queue theory. It also includes a method for selecting the timing of key microservice rotations to guide the selection and rotation timings of critical microservices. Finally, we introduced a multi-dimensional MTD heterogeneous rotation algorithm, grounded in heterogeneity and service efficiency, and conducted simulations using Python. Experimental results indicate that our proposed algorithm reduces defense latency by approximately 50% com pared to dynamic scaling and that defense costs stabilize after the initial defense against an attack, preventing continuous growth. [ABSTRACT FROM AUTHOR]

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As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess "better or worse" system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption. [ABSTRACT FROM AUTHOR]