Service-oriented Applications Research Articles

Component-based Distributed Software Reconfiguration:A Verification-oriented Survey

Distributed software built from components has become a mainstay of service-oriented applications, which frequently undergo reconfigurations to adapt to changes in their operating environment or their functional requirements. Given the complexity of distributed software and the adverse effects of incorrect reconfigurations, a suitable methodology is needed to ensure the correctness of reconfigurations in component-based systems. This survey gives the reader a global perspective over existing formal techniques that pursue this goal. It distinguishes different ways in which formal methods can improve the reliability of reconfigurations, and lists techniques that contribute to solving each of these particular scientific challenges.

TEACHING DEVOPS AND SOFTWARE ENGINEERING PRACTICES USING AN AUTOMATED PROGRAMMING ASSESSMENT SYSTEM

The software industry has seen a shift in recent years, with the adoption of agile development practices, the deployment of cloud-based, and service-oriented applications using DevOps. This has led to increased pressure on educators in higher education institutions to provide students with the best possible preparation for their careers, based on industry best practices. The research proposed a new approach to bridge the gap between in-class exercises and industrial practices by introducing an automated programming assessment system, ProgEdu. ProgEdu simulates the continuous integration pipeline of DevOps, incorporating unit testing and code quality assessment. It is deployed as a service-oriented application on Docker, allowing students to gain practical experience in a real-world setting. Our approach goes beyond simply assessing code, as we have also incorporated the service-oriented architecture, continuous integration pipeline, and Docker of the system to illustrate various DevOps practices in our software engineering classes. By providing hands-on exercises that utilize industry-standard tools and techniques, we can bridge the gap between theoretical knowledge and practical application, helping students to extensively understand fundamental concepts and prepare for the demands of the modern workforce.

STUDY OF METHODS OF CREATING SERVICE-ORIENTED SOFTWARE SYSTEMS IN AZURE

The modern development of service-oriented software systems is accompanied by the wide use of cloud technologies, which affect the competitiveness of companies and their systems, which provide opportunities to expand the client base thanks to the coverage of several regions of the city or country.
 The advantage of cloud services is availability in any part of the world where there is an Internet connection. Cloud providers provide a large volume of services for various needs: such as hosting, deployment of containers, file storage, databases, etc.
 In particular, all the most popular cloud providers offer several options for creating service-oriented software systems, including both standard technologies and proprietary developments. This paper compares the methods of creating service-oriented software systems based on the Azure cloud platform: Azure Container Apps, Azure Kubernete Service, and Azure Red Hat OpenShift. The subject area of technologies for the implementation of service-oriented application architecture is considered, and criteria for the analysis of methods for implementing applications with such an architecture are proposed. A software solution for comparing methods of creating service-oriented applications based on the Azure cloud platform was designed and developed. The developed software system provides an opportunity to rent scooters, bicycles and cars. 
 The purpose of the study is a comparative analysis of the methods of creating service-oriented software systems based on Azure services, and the subject of the study is a software solution implemented using these methods.
 The purpose of this work will be the development of a software system that will provide an opportunity to rent scooters, bicycles and cars. Using this system, we will investigate the deployment of this system on certain services from Azure.
 The results of this research on Azure services: Azure Container Apps, Azure Kubernete Service and Azure Red Hat OpenShift can be used when creating a new software system, when expanding an existing software system, when transferring software system components from other platforms to the Azure platform using these services.

A Momentum-Accelerated Hessian-Vector-Based Latent Factor Analysis Model

Service-oriented applications commonly involve high-dimensional and sparse (HiDS) interactions among users and service-related entities, e.g., user-item interactions from a personalized recommendation services system. How to perform precise and efficient representation learning on such HiDS interactions data is a hot yet thorny issue. An efficient approach to it is latent factor analysis (LFA), which commonly depends on large-scale non-convex optimization. Hence, it is vital to implement an LFA model able to approximate second-order stationary points efficiently for enhancing its representation learning ability. However, existing second-order LFA models suffer from high computational cost, which significantly reduces its practicability. To address this issue, this paper presents a Momentum-accelerated Hessian-vector algorithm (MH) for precise and efficient LFA on HiDS data. Its main ideas are two-fold: a) adopting the principle of a Hessian-vector-product-based method to utilize the second-order information without manipulating a Hessian matrix directly, and b) incorporating a generalized momentum method into its parameter learning scheme for accelerating its convergence rate to a stationary point. Experimental results on nine industrial datasets demonstrate that compared with state-of-the-art LFA models, an MH-based LFA model achieves gains in both accuracy and convergence rate. These positive outcomes also indicate that a generalized momentum method is compatible with the algorithms, e.g., a second-order algorithm, which implicitly rely on gradients.

A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

DeepLTSC: Long-Tail Service Classification via Integrating Category Attentive Deep Neural Network and Feature Augmentation

With the explosive growth in the number and diversity of Web services, correlative research has been investigated on Web service classification, as it fundamentally promotes advanced service-oriented applications, such as service discovery, selection, composition and recommendation. However, conventional approaches are restricted to indiscriminatingly classify Web services, which can trigger many challenges. First, they have not made full advantage of the implicit relationships among multi-dimensional information of Web services, such as the increasing number of service categories. Thus, it leads to low effectiveness of learning and representing service features, failing to ensure the overall accuracy of service classification. Second, the imbalance of service distributions has been ignored, while it is observed that service categories reveal distinct long-tail characteristics. That results in low accuracy on service classification for those categories that contain fewer Web services. To handle the challenges of more effectively learning implicit service features across the service repository, and with a particular concentration on those tail categories that contain fewer Web services, we propose a novel framework called DeepLTSC to more accurately perform the task of Web service classification under long-tail distributions. In DeepLTSC, we first present an improved label attentive convolutional deep neural network (LACNN) with service categories, which can generate deep service features to improve the overall classification performance. Then, a proposed service feature augmentation model (SFA) together with focal loss function is integrated into DeepLTSC to further optimize service features, aiming to boost the classification accuracy on tail service categories. Extensive experiments are conducted on three large-scale real-world services datasets with different long-tail distributions. The results demonstrate that DeepLTSC significantly outperforms state-of-the-art approaches for Web service classification on both overall and tail categories.

Assessing cloud QoS predictions using OWA in neural network methods

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Edge computing is becoming increasingly commonplace, as consumer devices become more computationally capable and network connectivity improves (e.g., due to 5G). With the rapid development of edge computing and Internet of Things (IoT), the use of edge-cloud collaborative computing to provide service-oriented network application (i.e., task) in edge-cloud IoT has become an important research topic. In this paper, we present an edge-cloud collaborative computing framework and our resource deployment algorithm with task prediction (RDAP). Based on our paradigm, tasks in the cloud service center are predicted using the two-dimensional time series, and task classification aggregation and delay threshold determination are combined to optimize task resource deployment of edge servers. A task scheduling algorithm with Pareto improvement (TSAP) is also proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of user’s quality of service and effect of system service to optimize task scheduling. The experimental results show that for varying user task scales and different Zipf distribution α parameters, combining RDAP and TSAP (RDAP-TSAP) can improve the average user task hit rate. In addition, the average task completion time of users, the overall system service effect, and the total task delay rate of RDAP-TSAP are better than TSAP and the benchmark algorithms for task scheduling.

Konteneryzacja usług Django i Postgres z wykorzystaniem docker-compose

The article shows the process of placing applications that provide web services in Docker containers, which allows you to significantly reduce the time to set up and maintain a work environment. Docker enables real-time scaling of the application infrastructure and improves resource utilization by supporting multiple containers. It also provides building of event-driven service-oriented applications. The whole thing allows you to focus on your business goals. The following chapters examine the Docker-based technology. They describe the steps of preparing the environment for building the configuration file and starting the container from creating the Dockerfile file, docker-compose.yml, through database configuration and starting the service itself.

The LRA Workbench: an IDE for efficient REST API composition through linked metadata

The number of Web APIs for accessing information and services is continuously increasing, and yet, no tools exist to automate the time-consuming and error-prone process of invoking those APIs and composing their responses. The recent emergence of widely-adopted, standardized, Web-API description formats and the development of Linked Data technologies for data integration have motivated our work on the LRA (Linked REST APIs) methodology [1, 2]. LRA relies on RDF service specifications to automate the development process around the usage of Web APIs. This automation represents a great opportunity to systematize and improve the quality of service-oriented application development. However, LRA’s reliance on SPARQL as the user-interaction model may hinder its adoption, because it requires developers to learn the intricacies of the unconventional graph data model and its associated datasets. In this paper we have developed the LRA Workbench (LRA_{Wbench}), which takes advantage of the emergent schema of Web-API specifications, in order to simplify the formulation of LRA-compliant SPARQL queries. Our empirical evaluation of the LRA_{Wbench} usability demonstrates that our tool significantly improves the performance of developers formulating SPARQL queries for LRA. A subsequent study on the effectiveness of the LRA_{Wbench} demonstrated that developers using LRA tend to produce code with considerable better structural complexity, in less time, than developers manually composing APIs.

Decentralized Anonymous Authentication With Fair Billing for Space-Ground Integrated Networks

The space-ground integrated network (SGIN) has attracted growing attention due to its advantages of high-capacity, low-latency and global coverage. To guarantee the security requirements of SGIN, anonymous authentication is an essential approach to addressing severe threats such as unauthorized access and impersonation attack, while payment is another common method in service-oriented applications in order to encourage companies to participate and also prevent insider attackers from enjoying services without paying. Existing anonymous authentication protocols for SGIN either require users to involve in heavy computation especially in dynamic scenarios, or do not consider cross-domain authentication. In this paper, we first introduce the concept of cross-company satellite services, and then address the cross-domain authentication and billing issues in SGIN, i.e., we propose a decentralized anonymous authentication scheme supporting fast handover and also achieve fairness for the billing procedures. To reduce the authentication delays, the authentication process is delegated from ground to satellites, such that users can arbitrarily switch the satellite networks belonging to different companies. Specifically, users can be anonymously authenticated by showing the knowledge of a secret bound with a randomized verifiable credential and connect to different satellite networks through a fast handover authentication protocol building on a blockchain. Even all companies collude, they cannot forge a valid identity credential and access records for a user. In addition, we provide a fair billing mechanism that can prevent malicious users and greedy satellite companies from manipulating the network accessing fees based on a well-designed smart contract. Finally, we demonstrate that the proposed scheme is secure and efficient through security analysis and performance evaluation.

Dealing With Context Awareness for Service-Oriented Systems

In recent years, rapid advances in the enabling technologies for mobile and ubiquitous computing, software paradigms, embedded sensor technologies, and wide range of wired and wireless protocols have been witnessed. Specifically, context-aware services-oriented applications are emerging as the next computing paradigm in which infrastructure and services are seamlessly available. Context-awareness, being an important ingredient, plays a vital role in enabling such interactive smart environments. More recently, the increasing popularity of ontologies has led to new ontology-based models of context because of their potential to support sophisticated ontology-based reasoning methods. This paper presents an architecture for the development of context-aware services based on ontologies. The authors highlight the context metamodel and discuss about reasoning process. This research also presents the semantic approach for service adaptation in context aware environment.

Availability-Aware Multi-Objective Cluster Allocation Optimization in Energy-Efficient Datacenters

With increasing network virtualization, data centre's workloads are modified in depth to serve various service-oriented applications, often defined by a time-bound service response, which, in turn, places a heavy demand on data center networks. Network virtualization in computing is the technique of integrating network resources and network functions in hardware and software into one virtual network, the software-based administration entity. Number of people ask for the server simultaneously, thereby slowing down the service.It is so costly to buy a new server that we developed a virtual system by creating a virtual system. With a trend to increase the number of cloud apps in the datacenter. There are numerous physical machines (PMs) linked via switches in the datacenter. Hardware PM resources for adaptable and elastic computing capabilities are usually shared via virtualization technology. Usually a cloud application is implemented in a virtual cluster that includes many virtual machines which occupy PM resources on request.

Dependence-Based Data-Aware Process Conformance Checking

Data-aware executable processes are an effective and efficient means to build service-oriented applications. However, since the services involved are loosely-coupled and self-managed, the process is flexible by nature and it executions may deviate from their specifications. In contrast to existing approaches that focus on control flow deviations, we leverage activity dependences for data-aware process conformance checking. To analyze the conformance of a process instance to its process definition, we seek a process reference trace “best-fitting” the instance trace such that the conformance degree of the input trace to the process equals the consistency degree of both traces. We measure the consistency between two traces based on their activity dependences. Since finding the reference trace is NP-hard, we resort to heuristics based on process decomposition and trace replaying to determine the trace. Our approach can identify conformance decrease caused by activity dependence deviations, thus, complementing existing approaches. We implement our approach as a ProM plugin. Experimental results on 102 real-world WS-BPEL processes and 26,880 synthetic input traces confirm the effectiveness and efficiency of our approach.

Generalized Nesterov's Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis

A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service-oriented applications. However, its convergence rate is slow. To address this issue, this study proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> eneralized Nesterov's acceleration-incorporated, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> on-negative and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> atent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> actor (GNALF) model. It results from a) incorporating a generalized Nesterov's accelerated gradient (NAG) method into an SLF-NMU algorithm, thereby achieving an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> AG-incorporated and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> lement-oriented <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</u> on-negative (NEN) algorithm to perform efficient parameter update; and b) making its regularization and acceleration parameters self-adaptive via incorporating the principle of a particle swarm optimization algorithm into the training process, thereby implementing a highly adaptive and practical model. Empirical studies on six large sparse matrices from different recommendation service applications show that a GNALF model achieves very high convergence rate without the need of hyper-parameter tuning, making its computational efficiency significantly higher than state-of-the-art models. Meanwhile, such efficiency gain does not result in accuracy loss, since its prediction accuracy is comparable with its peers. Hence, it can better serve practical service applications with real-time demands.

Service-oriented invisible numerical control application: architecture, implementation, and test

ABSTRACT CNC system has improved greatly upon machining precision and speed over the past six decades but remains vendor-proprietary and working in a stand-alone pattern. Open CNC aims to deal with the vendor-proprietary issue and numerous open CNC prototypes have been developed in the past three decades. But they are heterogeneous, low-flexible, and remain working in a stand-alone pattern. In today's Internet era, the stand-alone working pattern tends to evolve into cloud-based control pattern (cloud-CNC). On the basis of open CNC and cloud-CNC, our team proposes a novel CNC prototype called Invisible Numerical Control (INC). This paper aims to develop the INC application within the constraints of open CNC and cloud-CNC. Service-oriented paradigm is utilised to develop INC application at three levels. First, INC application is a software-as-a-service-style application. Second, CNC modules are exposed as services and CNC functionality is determined by service-oriented architecture. Third, INC application is developed using microservices architecture. Four autonomous INC microservices are defined and their mapping relationships are determined. All INC microservices are implemented as RESTful services and two complex INC microservices are implemented based on domain-driven design. A desktop client and its corresponding API gateway have been developed to test INC application.

NFMF: neural fusion matrix factorisation for QoS prediction in service selection

Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method.

Sova: A Software-Defined Autonomic Framework for Virtual Network Allocations

With the rise of network virtualization, the workloads deployed on data center are dramatically changed to support diverse service-oriented applications, which are in general characterized by the time-bounded service response that in turn puts great burden on the data-center networks. Although there have been numerous techniques proposed to optimize the virtual network allocation in data center, the research on coordinating them in a flexible and effective way to autonomically adapt to the workloads for service time reduction is few and far between. To address these issues, in this article we propose Sova, an autonomic framework that can combine the virtual dynamic SR-IOV (DSR-IOV) and the virtual machine live migration (VLM) for virtual network allocations in data centers. DSR-IOV is a SR-IOV-based virtual network allocation technology, but its operation scope is very limited to a single physical machine, which could lead to the local hotspot issue in the course of computation and communication, likely increasing the service response time. In contrast, VLM is an often-used virtualization technique to optimize global network traffic via VM migration. Sova exploits the software-defined approach to combine these two technologies with reducing the service response time as a goal. To realize the autonomic coordination, the architecture of Sova is designed based on the MAPE-K loop in autonomic computing. With this design, Sova can adaptively optimize the network allocation between different services by coordinating DSR-IOV and VLM in autonomic way, depending on the resource usages of physical servers and the network characteristics of VMs. To this end, Sova needs to monitor the network traffic as well as the workload characteristics in the cluster, whereby the network properties are derived on the fly to direct the coordination between these two technologies. Our experiments show that Sova can exploit the advantages of both techniques to match and even beat the better performance of each individual technology by adapting to the VM workload changes.

Future Internet for Service Oriented Applications

As with growing internet, the objects in the system need to implant with the external environment. Every object is resided in distinct place and works in an interoperable way. This process of Smart living which involves various smart devices – Smart phones, Sensors, Actuators, Radio Frequency Identification (RFID) tags, etc., evolving in this modern era is coined as Future Internet or Internet of Things (IOT). This IOT associated with distinguished services and visions through which it can be identified among various domains. A distributed intelligence needs to be employed to the application specific machine in order to serve the human perception for the sake of smart forensics. This paper explores and interprets the next generation internet, its components, standards and services along with their applications in diversified fields. Authors provide a systematic exploration of existing IoT products in the marketplace and highlights several possibly meaningful research objectives and drifts.

Cloud-Based Service Oriented Architecture for Social Vehicular Ad-hoc Network Communications

This article offers a social networking platform for vehicles, based on cloud computing and service oriented architecture called CSS-IoV for communication and collaboration of vehicle to vehicle (V2V), and vehicle to infrastructure (V2I). CSS-IoV contains five layers: application layer, application service layer, services layer, cloud infrastructure layer, and layer of vehicles. The application layer operation is associated with the functionality of on-board units (OBUs) and road-side units (RSUs). The service layer runs on a service-oriented architecture (SOA) and cloud application services to improve applications. To expand and combine web services on this layer, the application developer and builders can effectively develop the new functions and programs for communication. With the support of dynamic and automated service collaboration, people can easily help and communicate with each other by using OBUs. Our results shows that the proposed scheme provide the best results in terms of both type of communication in VANETs.