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Microservice-based applications are increasingly being adopted along with cloud service models, and nowadays serve millions of customers daily. They are supported by container-based architectures which are managed by orchestration platforms, such as Kubernetes, that monitor, manage, and automate most of the tasks. Although these tools provide failover capabilities, it is not yet studied how effective they are in dealing with diverse types of faults. Fault injection is an effective methodology for validating components that are supposed to detect the malfunctions and report/correct them. This paper studies the effectiveness of Kubernetes in dealing with faults and aging in microservices, and on the possibility of using faults to accelerate aging effects for testing purposes. For this, we conducted an analysis of the implementation and tuning of Kubernetes probes, followed by experiments with varying load and fault injection into two distinct and representative microservice testbeds to analyze the capacity of probes in detecting issues in applications. The goal is to improve the knowledge of researchers and developers on whether Kubernetes can detect different faults and aging issues. Also, even though some services tend to accumulate aging effects, with increasing resource consumption, Kubernetes does not detect them nor acts on them, indicating that probes may be insufficient for aging scenarios. Results also showed that fault injection is useful to accelerate aging effects for the testing and evaluation purposes.

As microservices become more popular, more drawbacks become apparent to developers. One issue that many teams face today is the failure to visualize the entire system architecture holistically. Without a full view of the system, the architecture can become convoluted as teams add and subtract from their system without reconciling their changes. One established practice to determine a view on the entire system involves dynamic analysis of microservice interaction and dependencies. In this mapping study, we investigate dynamic analysis as a way to visualize system architecture. Capturing the architectural view with dynamic analysis has the ability to build the system and then show its behavior at run-time. We identify dynamic analysis techniques, the corresponding tools, and the models that these practices can generate. The findings of this study are relevant to developers of decentralized systems looking for a way to visualize their system architecture in a dynamic perspective.

Microservices architecture is a new paradigm for developing a software system as a collection of independent services that communicate via lightweight protocols. In greenfield development, identifying the microservices is not a trivial task, as there is no legacy code lying around and no old development to start with. Thus, identification of microservices from requirements becomes an important decision during the analysis and design phase. Use cases play a vital role in the requirements analysis modeling phases in a model-driven software engineering process. Use cases capture the high-level user functions and the scope of system. In this paper, we propose GreenMicro, an automatic microservice identification technique that utilizes the use cases model and the database entities. Both features are the artifacts of analysis and design phase that depict complete functionality of an overall system. In essence, a collection of related use cases indicates a bounded context of the system that can be grouped in a suitable way as microservices. Therefore, our approach GreenMicro clusters close-knit use cases to recover meaningful microservices. We investigate and validate our approach on an in-house proprietary web application and three sample benchmark applications. We have mapped our approach to the state-of-the-art software quality assessment attributes and have presented the results. Preliminary results are motivating and the proposed methodology works as anticipated in identifying functionally cohesive and loosely coupled microservice candidate recommendations. Our approach enables the system architects to identify microservice candidates at an early analysis and design phase of development.

The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization.

Emerging cloud-native technologies, such as container-runtime and container-orchestrator offer unprecedented agility in developing and running applications, especially when combined with microservice-style architecture. Several existing 5G-Telecom network products such as element Management System (e-MS), 5G-Core and 5G-Access are being redesigned to fit the microservice paradigm. Cloud environment allows enterprises to scale their application on-demand with minimum cost; however, it is often difficult to use containers without sacrificing many benefits that cloud-native technology offers. The e-MS is characterized to orchestrate 5G network elements (5GNEs) deployed nationwide, and systematically store terabytes of stateful data per second. Containers are characterized to have an ephemeral state, hence ‘stateful-ness’ aspect of e-MS makes orchestration complex. In this paper, different challenges around stateful storage selection, content placement and content retrieval operations within e-MS microservices are described. To overcome these challenges, to this end, we propose CASE - A Context-Aware Storage placement and retrieval Ecosystem - which enables context-based operations to be intrinsically supported by the underlying e-MS application. Our approach has been designed to maintain the location-independent philosophy of cloud-native by associating context information directly to 5GNE rather than fixed storage entities, thereby ensuring scalability. Through simulation with real data-set obtained from one of the world’s largest terrestrial telecom operator, we show that based on such location-independent context information, CASE with e-MS can facilitate high performance despite dynamic 5GNE count agility, stateless e-MS replication and stateful storage scaling, while not posing a significant signaling burden on the cloud environment.

Context. Since its proclamation in 2012, microservices-based architecture has gained widespread popularity due to its advantages, such as improved availability, fault tolerance, and horizontal scalability, as well as greater software development agility. Motivation. Yet, refactoring a monolith to microservices by smaller businesses and expecting that the migration will bring benefits similar to those reported by top global companies, such as Netflix, Amazon, eBay, and Uber, might be an illusion. Indeed, for systems that do not have thousands of concurrent users and can be scaled vertically, the benefits of such migration have not been sufficiently investigated, while the existing evidence is inconsistent. Objective. The purpose of this paper is to compare the performance and scalability of monolithic and microservice architectures on a reference web application. Method. The application was implemented in four different versions, covering not only two different architectural styles (monolith vs. microservices) but also two different implementation technologies (Java vs. C#.NET). Next, we conducted a series of controlled experiments in three different deployment environments (local, Azure Spring Cloud, and Azure App Service). Findings. The key lessons learned are as follows: (1) on a single machine, a monolith performs better than its microservice-based counterpart; (2) The Java platform makes better use of powerful machines in case of computation-intensive services when compared to.NET; the technology platform effect is reversed when non-computationally intensive services are run on machines with low computational capacity; (3) vertical scaling is more cost-effective than horizontal scaling in the Azure cloud; (4) scaling out beyond a certain number of instances degrades the application performance; (5) implementation technology (either Java or C#.NET) does not have a noticeable impact on the scalability performance.

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

Sleep monitoring using polysomnography (PSG) in hospitals can be considered expensive, so the preferable way is to use contactless and wearable sensors to monitor sleep daily by patients at home. In this study, the Internet-of-Things (IoT) platform was utilized for sleep monitoring with contactless or wearable sensors as an integrated system developed based on an event-driven and microservice architecture. Multiple services that respond to events are provided within the system. Electrocardiogram (ECG) data were used as the input in the sleep monitoring system. The combination of the weighted extreme learning machine (WELM) algorithm with particle swarm optimization (PSO) was used to process the ECG data, followed by fuzzy logic to measure sleep quality, then display the data on the dashboard. Based on the experimental results, the proposed architecture increased throughput by 34.76%, decreased response time by 55.85%, and reduced memory consumption by 37.26% per instance replication compared to the non-event-driven architecture. The accuracies of the sleep stage classification were 78.78% and 73.09% for the three and four classes, respectively, and the area under a receiver operating characteristic (ROC) curve (AUC) reached 0.89 for both the three and four class classifications.

Static code analysis of software systems has proven beneficial for a broad range of domains, including security assessments, coding practice, error detection, and others. However, as modern systems have grown in complexity and heterogeneity over the past few decades, advances in development frameworks have dominated. Rather than involving low-level language constructs, these frameworks typically focus on software components, including data entities, controllers, and endpoints. As a result, current code analysis approaches have become unsuitable for analyzing these modern systems due to their focus on low-level constructs in a single language. Thus, code analysis has become a far more complicated endeavor thanks to the plethora of languages, frameworks, and design approaches in modern software development. This paper presents a novel approach to solving the problem of being tied to a single language and its low-level constructs. The system’s source code is transformed into an intermediate representation called a language-agnostic abstract-syntax tree. This system representation is then assessed by generalized component parsers that extract relevant high-level information, such as components, from low-level structures. The design of the approach is presented here in detail, along with its evaluation in a case study involving two large, heterogeneous, cloud-native system benchmarks (Java and C++ microservices). The study demonstrates a unified identification approach to determine system data entities and endpoints. Utilizing higher-level constructs, such as components, can advance the current practice of system analysis to better face broader problems introduced by modern system development practices.

Many companies migrate to microservices because they help deliver value to customers quickly and continuously. However, like any architectural style, microservices are prone to architectural technical debt (ATD), which can be costly if the debts are not timely identified, avoided, or removed. During the early stages of migration, microservice-specific ATDs (MS-ATDs) may accumulate. For example, practitioners may decide to continue using poorly defined APIs in microservices while attempting to maintain compatibility with old functionalities. The riskiest MS-ATDs must be prioritized. Nevertheless, there is limited research regarding the prioritization of MS-ATDs in companies migrating to microservices. This study aims to identify, during migration, which MS-ATDs occur, are the most severe, and are the most challenging to solve. In addition, we propose a way to prioritize these debts. We conducted a multiple exploratory case study of three large companies that were early in the migration process to microservices. We interviewed 47 practitioners with several roles to identify the debts in their contexts. We report the MS-ATDs detected during migration, the MS-ATDs that practitioners estimate to occur in the future, and the MS-ATDs that practitioners report as difficult to solve. We discuss the results in the context of the companies involved in this study. In addition, we used a risk assessment approach to propose a way for prioritizing MS-ATDs. Practitioners from other organizations and researchers may use this approach to provide rankings to help identify and prioritize which MS-ATDs should be avoided or solved in their contexts.

Cloud systems and microservices are becoming powerful tools for businesses. The evidence of the advantages of offering infrastructure, hardware or software as a service (IaaS, PaaS, SaaS) is overwhelming. Microservices and decoupled applications are increasingly popular. These architectures, based on containers, have facilitated the efficient development of complex SaaS applications. A big challenge is to manage and design microservices with a massive range of different facilities, from processing and data storage to computing predictive and prescriptive analytics. Computing providers are mainly based on data centers formed of massive and heterogeneous virtualized systems, which are continuously growing and diversifying over time. Moreover, these systems require integrating into current systems while meeting the Quality of Service (QoS) constraints. The primary purpose of this work is to present an on-premise architecture based on Kubernetes and Docker containers aimed at improving QoS regarding resource usage and service level objectives (SLOs). The main contribution of this proposal is its dynamic autoscaling capabilities to adjust system resources to the current workload while improving QoS.

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

Microservice architectures separate a large-scale complex application into multiple independent microservices.These microservices with various technology stacks communicate with lightweight protocols to implement agile development and conti-nuous delivery.Since the application using a microservice architecture has a large number of microservices communicating with each other,the faulty microservice should cause other microservices interacting with the faulty one to appear anomalies.How to detect anomalous microservices and locate the root cause microservice has become one of the keys of ensuring the reliability of a microservice based application.To address the above issue,this paper proposes an anomaly propagation-based fault diagnosis approach for microservices by considering the propagation of faults.First,we monitor the interactions between microservices to construct a service dependency graph for characterizing anomaly propagation.Second,we construct a regression model between me-trics and API calls to detect anomalous services.Third,we get the fault propagation subgraph by combining the service dependency graph and the detected abnormal service.Finally,we calculate the anomaly degree of microservices with a PageRank algorithm to locate the most likely root cause of the fault.The experimental results show that our approach can locate faulty microservices with low overhead.

Microservices architecture has gradually become the primary consideration for the development of large software systems with scalability and flexibility. The orchestration and choreography patterns are provided to facilitate communications among microservices. Meanwhile, PHP is the programming language with the highest usage rate of the global web application servers. Nevertheless, there's still a lack of related PHP development resources in the field of microservices. This work explores how to apply the service orchestration pattern to orchestrate the communications among microservice endpoints using PHP programming language. A set of service orchestration libraries, called Anser, are proposed based on PHP and RESTful API architecture. Developers can easily adopt the orchestration design pattern by using Anser to develop web applications based on microservices. Through performance evaluation, we show that Anser could facilitate the stability of microservices applications with lower error rates. [ABSTRACT FROM AUTHOR]

The number of game experiences based on the player's location has increased recently due to proliferation of and innovation in related technologies. In the educational context, the goal of these games has been to increase the player's skills with respect to a particular topic. When pervasive or narrative components are added; however, complexity of construction and support increases, requiring a tool to manage the information both appropriately and dynamically. Space and pervasiveness of social interaction work together to achieve objectives through the exchange of information between several pervasive games to provide a better game experience. This paper presents a platform enabled to create game experiences based on the player's location to improve students' skills by applying the pervasive narrative. It then validates a game experience designed and built in a higher education environment. [ABSTRACT FROM AUTHOR]

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single signon authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment. [ABSTRACT FROM AUTHOR]

Currently, the Mexican avocado supply chain has some social limitations that make the traceability process a difficult task and severely limits the regions that can add their harvest to the international market. We hypothesize that modernizing the traceability process and improving the trust of the final user could help in opening the market to other regions. This paper describes the Mexican avocado supply chain characteristics, identifies the actors involved in the supply chain, and emphasizes the problems that the current actors have when exporting them to the US market. On this basis, we propose a technological solution system to automate the traceability process. The system was designed to comply with the authority and consumer requirements. It proposes a combination of the benefits of traditional data traceability using Microservices architecture with a new layer of Blockchain auditing that will add value to current and new actors in every step of the supply chain. We contribute by proposing a model that adds value to the avocado supply chain with the following characteristics: Integrity, auditing service, dual traceability, transparency, and a front-end application with trust user-oriented. Our proofs demonstrate that the blockchain layer does not represent a considered high extra transaction cost; it could be regarded as despicable for the economy of the consumer considering costs and benefits. [ABSTRACT FROM AUTHOR]

Reading and analyzing data from sensors are crucial in many areas of life. IoT concepts and related issues are becoming more and more popular, but before we can process data and draw conclusions, we need to think about how to design an application. The most popular solutions today are microservices and monolithic architecture. In addition to this choice, there is also the question of the technology in which you will work. There are more and more of them on the market and in each of them it is practically possible to achieve similar results, but the difference lies in how quickly it will be possible and whether the approach invented will turn out to be the most optimal. Making the right decisions at the beginning of application development can determine its path to success or failure. The main goal of this article was to compare technologies used in applications based on microservice architecture. The preparation of a book lending system, whose server part was implemented in three different versions, each using a different type of technology, helped to achieve this goal. The compared solutions were: Spring Boot, Micronaut and Quarkus. The reason for this research was to investigate projects using sensor networks, ranging from telemedicine applications to extensive sensor networks collecting scientific data, or working in an environment with limited resources, e.g., with BLE or WIFI transmitters, where it is critical to supply energy to these transmitters. Therefore, the issue of efficiency and hence energy savings may be a key issue depending on the selected programming technology. [ABSTRACT FROM AUTHOR]

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With the development of software technology, some complex mobile and Internet-of-Things (IoT) applications can be constituted by a set of microservices. At present, mobile edge computing (MEC) has been used for microservice provision to achieve faster response speed and less network pressure. Based on container technology, microservices can be easily deployed in the MEC environment, while multiple microservice instances in multiple locations need to be selected to provide services for a large number of users geographically distributed. How to fully consider the service response time, scheduling the startup and running strategies of microservice instances with the least resource cost for multiple mobile edge servers is the core problem of microservice composition. In this paper, we propose a multi-objective evolutionary approach (MSCMOE) based on improved NSGA-III to minimize the service access delay and network resource consumption in the process of microservice composition. In order to maintain the diversity of the population, we use the improved reference point strategy to enhance the computational efficiency of seeking elite solutions in the non-dominated layer. Experimental results based on a real data set of Shanghai Telecom demonstrate that MSCMOE can effectively reduce network resource consumption while reducing service request time. [ABSTRACT FROM AUTHOR]

The article discusses the creation of an information-analytical system for evaluating the scientific performance of structural units of universities and research institutes based on the approach of constructing complex integral evaluation. A model of information technology for evaluating the results of scientific activity is proposed, consisting of four modules: an information collection module, an information storage module, an analytical module, and a module for user interaction and data visualization. The modular structure of the technology will allow expanding and modifying the capabilities of each of the modules independently of the others, as well as increasing the stability and flexibility of the technology. The implementation of this system is performed using microservices technology. A conceptual model of the information system and a structural model of the functioning of the information collection module, as well as a structural model of the information system database, are proposed. It is shown that most of the well-known indices for evaluating the performance of subjects of scientific activity, for example, h-index, g-index, e-index, I-10 index, etc., do not fully take into account information about citation. Therefore, the method for calculating the evaluation of scientific research activities of scientists was proposed, which does not lose information about any citation of the author and publication. This method determines the scalar evaluation of the results of scientific activity, and it is based on determining a few coefficients. The coefficients define one scientist's citation in the publications of other scientists. As a result, assessment is obtained by solving a system of linear algebraic equations that are constructed based on calculated coefficients. Most of the known evaluation approaches have their own calculation features and disadvantages, which are associated with the loss of some information. Therefore, it is not recommended to give preference to one of them. For the purposes of a comprehensive assessment of the productivity of research activities of scientists, the authors proposed a method of vector evaluation of the results and the construction of the integral assessment. This method is based on the construction of vectors and scalar estimates for each scientist in a multidimensional metric space. The dimensionality of the space is determined by the number of calculated scalar estimates. The method is also based on the construction of an ideal point, which consists of scalar estimates that are the best in terms of achieving maximum performance. The assessment of each subject of scientific activity is calculated as the metric distance from the ideal point to the vector of scalar estimates of this subject of scientific activity. [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.