Traditional Web Services Research Articles

Blockchain Based Decentralized Web Hosting System

Abstract: In today’s digital era, the internet heavily depends on a centralized framework where powerful servers support content on well-known platforms such as Facebook and Twitter. Although this centralization is proficient in content delivery, it comes with significant drawbacks, including a single point of failure and vulnerability to censorship. The need for a decentralized web hosting structure is growing due to its promise of content accessibility regardless of a user’s geographical location, while still maintaining the speed and reliability of conventional web services. Currently, the internet’s structure is defined by centralization, introducing risks related to cyber-attacks and service disturbances. For example, even a brief disruption to a large platform like Facebook can leave millions without internet access. Additionally, large corporations and government bodies possess significant control over the internet, enabling them to alter and censor content. To confront these problems, it becomes essential to develop a decentralized web structure that is publicly owned. This study delves into the issues arising from the centralized internet and investigates potential solutions by utilizing blockchain technology to decentralize web structure, subsequently improving traditional web services and fortifying the internet’s robustness and accessibility

Cost Efficiency Evaluation of an On-Chain, Decentralized Ride-Sharing Platform

Traditional taxi providers are slowly losing their clients to online taxi providers, which are becoming increasingly popular. These companies typically provide a commercial, centralized platform in order to provide these services. The concept of centralization gives those providers significant power over their users, such as demanding and changing fees and collecting data over provided services. We aim to discuss how a decentralized approach using blockchains can solve those problems. A ride-sharing platform with the most essential functionality is implemented using a decentralized application based on smart contracts, which are, in turn, deployed on different blockchains. Various effects with respect to implementing and providing a decentralized ride-sharing service are taken in to account, especially regarding the effective cost for users. The fees of the developed platform heavily depend on which blockchain it is deployed on. Blockchains may compete in term of cost with traditional ride-sharing platforms, but choosing a suitable blockchain for the application is essential. Furthermore, the prototype shows that the time it takes for a transaction to be manifested into a block and subsequently confirmed is multiple times longer than a traditional web service response. These new problems require a new approach to implementing those requests that do not negatively affect the user experience.

A Semantic Matching Method of E-Government Information Resources Knowledge Fusion Service Driven by User Decisions

This study focuses on the knowledge fusion model of e-government information resources that supports user decision-making information needs, it discusses the user decision-making information needs model, the knowledge fusion service model, and the relationship between them. The inter-layer mapping matching mechanism realizes the ultimate value of knowledge fusion. Therefore, this paper analyses and studies the mapping mechanism between the user information demand model and the knowledge fusion service model. A semantic, similarity-based knowledge fusion service matching method for e-government information resources is proposed to address the problem of lack of semantics in traditional web service matching methods. This method uses the ontology description language OWL-S to map information requirement documents of user decisions and knowledge fusion service function documents into an ontology tree structure. The authors then use this as the basis to calculate the concept similarity and relationship similarity measures, and the service matching based on semantic similarity can be realized.

LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured Features

Classifying Web services plays a critical role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. However, traditional Web service classification approaches usually difficult to dispose unstructured sparse documents and underutilize the rich network relations. The consideration of multiple document representation schemes can ameliorate the former problem, whereas an appropriate network representation method could be a positive solution to the latter problem. In this paper, we propose a general Web service classification framework via deep fusion of structured and unstructured features, named LDNM. Firstly, we transform each service document into feature vectors by using two document representation methods: topic distribution based on LDA, and neural-network-based document embedding model known as Doc2vec. Then we obtain structured representation vectors which stem from service invoking and tagging graphs by applying Node2vec. Finally, we fuse these features and train a service classifier by using an MLP neural network. Comprehensive experiments are conducted on real-world datasets to demonstrate the effectiveness of the proposed approach.

Elastic Performance Test Method of Web Server in Cloud Computing Environment

In the process of traditional web server elastic performance test, the performance of tracking data is poor, which leads to excessive server jitter and poor accuracy of elastic test results. Therefore, this paper studies the network server elastic performance test method in cloud computing environment. In this method, cloud monitoring technology is used to track the operation data of Web server. According to the multi platform call mode of Web server, a statistical regression model is established to determine the elasticity measurement index. The load balancing algorithm is used to test the server load balancing to obtain the elasticity value of Web server. Experimental results: in the whole running period of Web server, the jitter times of the proposed method are 15.6066 times, 16.5600 times and 16.5733 times lower than those of the three traditional methods, respectively. It can be seen that the new test method can accurately track the response ability of the server and obtain more accurate elasticity value.

Особливості масштабування веб-додатків

Node.js - це відносно нова платформа для розробки веб-додатків, серверів додатків, будь-якого типу мережевих серверних або клієнтських модулів, а також розробки програм загального призначення. Вона спроектована з можливістю максимальної масштабованості мережевих додатків завдяки оригінальному поєднанню можливостей серверної мови JavaScript, асинхронного введення-виведення і асинхронного програмування. У той час, коли в багатьох платформах потоки широко використовуються для масштабування додатків з метою максимального завантаження CPU, Node.js уникає потоків через їх внутрішню складність і великі накладні витрати при безкінечних переключеннях між ними. Додатки на платформі Node.js зазвичай потребують масштабування раніше, ніж традиційні веб-сервери, щоб забезпечити можливість користування всіма ресурсами додатка. Це змушує розробників розглядати можливість масштабування на самих ранніх етапах розробки й пересвідчуватися, що додаток не покладається на ресурси, які не можуть спільно використовуватися декількома процесами або комп’ютерами. Екземпляри додатків не повинні зберігати інформацію на ресурсах, які не підтримують спільного використання, таких як пам'ять або диск, що фактично є обов'язковою умовою масштабування. Масштабування сприятливо діє на інші характеристики додатка, зокрема, на надійність і відмовостійкість, бо дозволяє гарантувати певний рівень обслуговування навіть при наявності неполадок і збоїв. Коли запускається кілька екземплярів одного і того ж додатка, формується надлишкова система, тобто, якщо один з екземплярів припинить роботу, інші продовжать обслуговування запитів. Цю схему легко реалізувати за допомогою модуля cluster в додатках Node.js. Альтернативою використання модуля cluster є запуск декількох автономних екземплярів одного і того ж додатка, які прослуховують різні порти або виконуються на різних комп'ютерах, і використання зворотного проксі-сервера для доступу до екземплярів і розподілу трафіку між ними. Ще одним варіантом є розподіл додатку на відносно автономні компоненти, які оформлені як окремі, незалежні додатки, що мають власні бази даних. В статті показано, що можна виконувати масштабування веб-додатків шляхом їх декомпозиції на підставі виконуваних ними функцій і сервісів. Ця методика дозволяє масштабувати не тільки пропускну здатність додатків, але, що головніше, їх складність.

SARL: A reinforcement learning based QoS-aware IoT service discovery model

Abstract The IoT environment includes the enormous amount of atomic services with dynamic QoS compared with traditional web services. In such an environment, in the service composition process, discovering a requested service meeting the required QoS is a di cult task. In this work, to address this issue, we propose a peer-to-peer-based service discovery model, which looks for the information about services meeting the requested QoS and functionality on an overlay constructed with users of services versus service nodes, with probably constrained resources. However, employing a plain discovery algorithm on the overlay network such as flooding, or k-random walk could cause high message overhead or delay. This necessitates an intelligent and adaptive discovery algorithm, which adapts itself based on users’ previous queries and the results. To fill this gap, the proposed service discovery approach is equipped with a reinforcement learning-based algorithm, named SARL. The reinforcement learning-based algorithm enables SARL to significantly reduce delay and message overhead in the service discovery process by ranking neighboring nodes based on users’ service request preferences and the service query results. The proposed model is implemented on the OMNet simulation platform. The simulation results demonstrate that SARL remarkably outperforms the existing approaches in terms of message overhead, reliability, timeliness, and energy usage efficiency.

Web Service Composition Optimization Based on Adaptive Mutant Beetle Swarm

with the continuous development of cloud computing technology and big data, web services are widely used in intelligent information processing in all walks of life, when facing a large number of services with similar functions and quality, web service composition technology is a crucial technology in web services, which enable common services to be combined and select the optimal service combination to meet the wide range of users’ needs. Aiming at the problems of algorithm accuracy and stability of existing service composition technologies, the penalty function is used to construct the fitness function in this paper, which transforms the constrained optimization problem into an unconstrained optimization problem, and realizes the global optimization. Traditional web service composition models are aimed at QoS attribute perception. This paper establishes a QoE model that focuses on user experience, which can better meet the needs of users. An adaptive mutant beetle herd algorithm was proposed in this paper, the author introduced Beetle Antennae Searching Algorithm into the common particle swarm optimization algorithm, and added an adaptive mutation factor to enhance the searching ability of the algorithm; The use of dynamic learning factors allows the algorithm to have better population diversity and enhanced convergence. The experimental results that the algorithm proposed in this paper has higher algorithm accuracy, comparatively faster convergence speed and stability.

Проблемы и перспективы смешанного обучения в вузе в период диджитализации

The author addresses the urgent problem of the synthesis of traditional and e-learning and considers one of the promising formats of study at a university - blended learning. The terminological analysis revealed analogues to this training: blend training, mix training, blended learning, hybrid learning, integrated or web-based advanced learning. Its essence is a reasonable combination of offline and online technologies, methods, interaction techniques and tools. The methodological basis of the study was a comprehensive, integrative-competency and professional approaches. Turning to numerous domestic and foreign studies, the author proves the inevitability and popularity of this form of education. One of the varieties of this approach is described - inverted learning. In blended learning in the digital age, along with traditional technologies, web services and platforms, social networks, instant messengers, distance learning systems, collaboration environments, smart contracts, etc. are actively used. The article outlines the advantages, problems and prospects of the blended learning concept. One of the acute problems is the online training of teachers. Minister of Science and Higher Education of the Russian Federation V. Falkov urges urgent training of all teachers by 100%.

On the use of big data frameworks for big service composition

Over the last years, big data has emerged as a new paradigm for the processing and analysis of massive volumes of data. Big data processing has been combined with service and cloud computing, leading to a new class of services called “Big Services”. In this new model, services can be seen as an abstract layer that hides the complexity of the processed big data. To meet users' complex and heterogeneous needs in the era of big data, service reuse is a natural and efficient means that helps orchestrating available services' operations, to provide customer on-demand big services. However different from traditional Web service composition, composing big services refers to the reuse of, not only existing high-quality services, but also high-quality data sources, while taking into account their security constraints (e.g., data provenance, threat level and data leakage). Moreover, composing heterogeneous and large-scale data-centric services faces several challenges, apart from security risks, such as the big services' high execution time and the incompatibility between providers' policies across multiple domains and clouds. Aiming to solve the above issues, we propose a scalable approach for big service composition, which considers not only the quality of reused services (QoS), but also the quality of their consumed data sources (QoD). Since the correct representation of big services requirements is the first step towards an effective composition, we first propose a quality model for big services and we quantify the data breaches using L-Severity metrics. Then to facilitate processing and mining big services' related information during composition, we exploit the strong mathematical foundation of fuzzy Relational Concept Analysis (fuzzy RCA) to build the big services' repository as a lattice family. We also used fuzzy RCA to cluster services and data sources based on various criteria, including their quality levels, their domains, and the relationships between them. Finally, we define algorithms that parse the lattice family to select and compose high-quality and secure big services in a parallel fashion. The proposed method, which is implemented on top of Spark big data framework, is compared with two existing approaches, and experimental studies proved the effectiveness of our big service composition approach in terms of QoD-aware composition, scalability, and security breaches.

IoT Service Recommendation Scheme Based on Matter Diffusion

With the development of Internet of Things(IoT), more and more smart devices and services appeared in people's life. These services make life more convenient, while it is difficult for people, especially the elderly, to find them in time, because they are widely distributed in great numbers with small sizes. In order to solve this problem, the service recommendation scheme, which is becoming increasingly important, needs to be used in IoT. However, the traditional web service recommendation schemes are not suitable for the IoT, because they rely more on the historical information rather than the energy state or the user's habit attributes, which are important for IoT. We research the service recommendation scheme, finding that the service recommendation process in IoT is a process of matter flows, which follows the conservation of matter. Therefore, we propose the service recommendation scheme based on tripartite graph with matter diffusion and use the habit feature as a dynamic tag. Based on the balance of matter, we use the positive and negative matter diffusion results on the tripartite as the recommendation results. The results of our evaluation show that the performance of the service recommendation scheme is improved in the precision and recall.

A server-side accelerator framework for multi-core CPUs and Intel Xeon Phi co-processor systems

Processing-intensive web server requests can lead to low Quality of Service (QoS), such as longer mean response time and lower throughput, which calls for a new web server software framework that can improve the performance of web servers. The characteristic of request-level parallelism in web servers is fit for many-core architecture accelerators, such as GPU and Intel Xeon Phi co-processors, but traditional web server model cannot make full use of the performance of these accelerators. We proposed a new web server software framework— called MIC-based Server-side Accelerator Framework (MSAF)—for a machine with not only multi-core CPUs but also Intel Xeon Phi co-processors based on Staged Event Driven Architecture (SEDA). The framework can fully exploit the performance of Intel Xeon Phi co-processors and multi-core CPUs, and improve power/energy efficiency by offloading the stage of handling requests to Intel Xeon Phi co-processors. We implemented the web server simulation software based on MSAF framework on a machine with multi-core CPUs and Intel Xeon Phi co-processors, and evaluated it by means of Apache Benchmark (AB). Our evaluation of MSAF shows its performance is about equivalent to that of a web server cluster consisting of four to five computing nodes. This paper indicates that if MSAF is applied to, Intel Xeon Phi co-processors are suitable for server side software, such as web servers, DNS servers, and database servers, because of its characteristic of lower communication latency between Intel Xeon Phi co-processors and host, more powerful logic processing ability, and more energy efficiency.

A user requirement oriented web service discovery approach based on logic and threshold petri net

In recent years, the number of Web services has increased significantly. Web service discovery has drawn much attention with the development of Web service applications and big data analysis. Under this circumstance, traditional Web service discovery strategies cannot adequately meet high user requirements due to the efficiency and precision of service discovery is low. In order to improve the accuracy and efficiency of service discovery, a user requirement oriented Web service discovery approach based on Petri nets is proposed in this study. A data preprocessing strategy of Web service is first designed. Then, a service clustering method is proposed based on Petri nets, which can conduct service cluster head generation, service cluster composition, and service discovery. The proposed method utilizes a superior data preprocessing method. Using simulation experiments, the efficiency and precision of Web service discovery are illustrated. Finally, the application value of the approach on real Web service is discussed.

A full comparison study of service discovery approaches for internet of things

PurposeThe purpose of this paper is to address the Internet of Things (IoT) service discovery problem and investigate the existing solutions to tackle this problem in many aspects.Design/methodology/approachThis paper presents an overview of IoT services aiming at providing a clear understanding about their features because this term is still ambiguous for the IoT service discovery approaches. Besides, a full comparison study of the most representative service discovery approaches in the literature is presented over four perspectives: the IoT information model, the mechanism of IoT service discovery, the adopted architecture and the context awareness. These perspectives allow classifying, comparing and giving a deeper understanding of the existing IoT service discovery solutions.FindingsThis paper presents a new definition and a new classification of IoT services and citation of their features comparing with the traditional Web services. This paper discusses the existing solutions, as well as the main challenges, that face the service discovery issue in the IoT domain. Besides, two classifications of the approaches are adopted on the basis of their service description model and their mechanism of discovery, and a set of requirements that need to be considered when defining an IoT service are proposed.Originality/valueThere are few number works that survey the service discovery approaches in the IoT domain, but none of these surveys discuss the service description models in the IoT or the impact of the context awareness aspect in the service discovery solution. There are also few works that give a comprehensive overview of IoT services to understand their nature to facilitate their description and discovery. This paper fills this gap by performing a full comparison study of multi-category and recent approaches for service discovery in the IoT over many aspects and also by performing a comprehensive study of the IoT service features.

Flexible service discovery based on multiple matching algorithms

Traditional web service discovery approaches rely on logic or non-logic matching techniques. In general, logic approaches can achieve satisfactory precision levels, but they result in modest recall scores. In contrast, non-logic approaches may ensure more balanced scores in terms of recall and precision, but they need additional aggregation schemes or optimisation methods. To improve the discovery performance, we need to combine multiple matching algorithms and fuse their results into a single ranked list of services. This combination must avoid the well-known side effects of fusion, such as overfitting or noise sensitivity. To tackle the service-discovery issue, we propose a solution based on two key ideas: first, we propose a majority voting model based on the 'Condorcet' paradigm to fuse a set of individual ranked lists (provided by the matching functions). Second, we leverage a probabilistic extension of the dominance relationship to ensure comparison between the services. The experimental evaluations indicate the proposed solution, 'probabilistic Condorcet', outperforms all individual matching functions, as well as many concurrent fusion algorithms.

A Factorization Machine-Based QoS Prediction Approach for Mobile Service Selection

Mobile services allow us to access the abundant and various resources (including data and services) on the Internet or devices in the physical world via wireless network technologies. It becomes increasingly popular to create mobile applications by combining existing mobile services. Mobile service selection is an important issue since different services with equivalent functions may have quite different qualities (e.g., performance). Even the same service may present different performances due to the volatility of mobile environments and move of users. Hence, getting aware of the quality of mobile services is a crucial need in service selection. To meet this need, a dozen of quality-of-service (QoS) prediction approaches have been proposed for traditional Web services and mobile services. However, their prediction accuracy and time efficiency still have plenty of room for improvement. This paper proposes a collaborative filtering approach to predict the QoS of mobile services based on factorization machines. Factorization machines significantly improve the traditional collaborative filtering techniques in both accuracy and time efficiency. The proposed approach revamps the classic factorization machine model by incorporating the locations of service users to better fit the mobile environments. The experimental results based on real-world QoS data show that the proposed approach outperforms the other collaborative filtering approaches.

A user preference awareness k-neighbour optimised selection algorithm: modelling and implementation

With the development of the massive amount of web services, the popular research area is involved with the solution to select the required services according to the personal preference for different users. To solve these urgent problems, a user preference awareness k-neighbour optimised selection algorithm is proposed in this paper. Initially, the potential interest of the user is explored by using association rules method of data mining technology. Then, an expanded LDA model is used to analyse the influence on user preference calculation for different types of services caused from the functional attributes of web services. To improve the accuracy of the service selection result, the ant colony algorithm is modified to optimise the service selection process of k-neighbours in traditional collaborative filtering. The experiment shows that our proposed method leads to a higher accuracy and coverage than the traditional web service selection methods based on the real service set.

Proposal of an integrated decision support system for Tuberculosis based on Semantic Web

Epidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly carried out to increase the integrality of healthcare. Otherwise, knowledge extraction and clinical and administrative decision-making processes are significantly hampered, directly affecting the management and quality of health services. Thus, this work aims to establish a computerized decision support system capable of collecting, integrating and sharing TB health data in Brazilian Unified Public Health System. Also, it will allow the monitoring of infected patients and the visualization of consolidated information of regular TB and its resistant variants for health professionals and managers. The data will be made available from heterogeneous, disconnected and unstructured sources by combining traditional web services, Semantic Web resources and security algorithms. A solid knowledge base applied to epidemiological surveillance, health information governance and clinical support will be enabled to integrate the multiple areas of TB patients care, as well as to support the creation of more accurate operational and diagnostics models.

Design and Implementation of Real-Time Management System Architecture based on GraphQL

In times of big data, the increasing growth of data sets is a big challenge for both data storage and data access, especially the management system with high real-time requirements. For real-time management system, there are some performance problems in the traditional Web service architecture based on REST style. In this paper,it proposes to establish GraphQL server in the real-time subsystem and integrate it into the existing systems.Through this architecture, the client can obtain the specified data from the server side, thus reducing the number of network requests and avoiding excessive capture or capture of data. Experiments are carried out in a real-time monitoring system. The experimental results show that the proposed architecture reduces the transmission data packets by 57.38% in rea-time monitoring and management system, and reduces the average time of data transmission by 8.9%.

Web Applications and Web Services: A Comparative Study

Software components that rely on the Internet in order to be accessed and used cover many aspects of our daily activities including email checking, weather checking, purchase ordering, and Facebook logging. Nowadays, these components are considered one of the most valuable and fundamental rights in human’s life. However, accessing and using the functionality of such components are performed using two techniques: web applications or traditional web services. Determining which one of the techniques is better suited for delivering the functionality of an Internet-based software component is not an easy task to decide. Therefore, this paper aimed to make a comparative analysis and study of both techniques alongside many directions to help web developers make the right choice to deliver the functionality of Internet-based software components. This is achieved by providing them with a set of requirements that have been proposed by the authors. The proposed requirements clarify a number of misunderstandings and issues peculiar to both techniques. Furthermore, this paper has provided a comparative table of both techniques. To the best of our knowledge, so far there is no comprehensive comparative study has been conducted in this context which was the rationale for the authors to carry out this study.