Number Of Web Services Research Articles

A Comparative Assessment of JVM Frameworks to Develop Microservices

With the ever-increasing wide spread of the Internet, the number of web services, web applications, and IoT devices is growing every year. This brings a number of challenges, both in terms of network bandwidth and the ability to scale individual computing nodes, whether they are large systems running in computing clouds or smaller IoT devices running closer to their data sources (so-called edge computing). In both cases, the way to cope with handling large numbers of users/requests is horizontal scaling, the implementation of which today is using the concept of microservices. However, the concept itself is not enough—we need ready-made application frameworks that allow us to easily implement and deploy efficient services. In the case of the Java ecosystem, which is one of the most mature platforms for enterprise-class software development, several frameworks dedicated to the development of microservices have been engineered recently. These tools support system developers in implementing communication, computation, and data storage mechanisms. However, so far, there is a lack of comparative analysis of individual solutions in the scholarly discourse to assess their performance and production maturity, so the authors in this article try to fill this gap. Based on synthetic tests developed by the authors, the most promising frameworks (Spring Boot, Micronaut, Quarkus) were analyzed both in terms of computational, compilation, or deployment performance. The results obtained can help system architects make rational and evidence-driven choices of system architecture and technology stacks.

Web Service Recommendation Based on Word Embedding and Node Embedding

Thanks to the rapid development of service-oriented computing (SOC) technologies, the number of Web services, such as Web API, is increasing rapidly. However, this brings some difficulties for mashup (a kind of Web API composition) developers to choose appropriate Web Services to build their projects. Finding required Web APIs from a large number of candidates and recommending them to developers has become a vital issue in mashup development. The traditional collaborative filtering algorithm has the problems of cold start and sparse data. In order to solve the deficiency of the collaborative filtering algorithm, we propose an improved hybrid method that combines the two kinds of information to generate word embedding and node embedding, avoiding the cold start problem and data sparsity problem. Experiments on real-world data sets show that our proposed approach is better than five state-of-the-art approaches, which verifies the effectiveness of our approach.

Using Ensemble and TOPSIS with AHP for Classification and Selection of Web Services

Due to the increasing number of Web Services with the same functionality, selecting a Web Service that best serves the needs of the Web Client has become a tremendously challenging task. Present approaches use non-functional parameters of the Web Services but they do not consider any preprocessing of the set of functionally Similar Web Services. The lack of preprocessing results in increased use of computational resources due to unnecessary processing of Web Services that have a very low to no chance of satisfying the consumer’s requirements. In this paper, we propose an Ensemble classification method for preprocessing and a Web Service Selection method based on the Quality of Service (QoS) parameters. Once the most eligible Web Services are enumerated through classification, they are ranked using the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) method with Analytic Hierarchy Process (AHP) used for weight calculation. A prototype of the method is developed, and experiments are conducted on a real-world Web Services dataset. Results demonstrate the feasibility of the proposed method.

An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack Algorithms

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.

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.

A Survey on Web Service Discovery Approaches

Service Oriented Architecture (SOA) is an approach to build distributed systems that deliver application functionality as services that are language and platform-independent. Web service is one of the fundamental technologies in implementing SOA based applications. Web services are modular, self-describing, self-contained and loosely coupled applications that can be published, located, and invoked across the web. As the number of web services is increased, finding a set of suitable web service candidates with regard to a user’s requirement becomes a challenge. Web service discovery is the process of finding the most suitable service by matching service descriptions against service requests. Various approaches for web service discovery have been proposed. In this paper, we present an overview of different approaches for web service discovery described in the literature and try to classify them into different categories. We also determine the advantages and disadvantages of each category. The goal is to help researchers to propose a new approach or to select the most appropriate existing approach for service discovery.

CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering

With the proliferation of Internet-of-Things and continuous growth in the number of web services at the Internet-scale, the service recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service recommendation is the Quality-of-Service (QoS) parameter, which depicts the performance of a web service. In general, the service provider furnishes the value of the QoS parameters before service deployment. However, in reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task, and thus, the QoS prediction has gained significant research attention. Multiple approaches are available in the literature for predicting service QoS. However, these approaches are yet to reach the desired accuracy level. In this article, we study the QoS prediction problem across different users, and propose a novel solution by taking into account the contextual (more specifically, location) information of both services and users. Our proposal includes two key steps: (a) hybrid filtering, and (b) hierarchical prediction mechanism. On the one hand, the hybrid filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical prediction mechanism is to estimate the QoS value accurately by leveraging hierarchical neural-regression. We evaluated our framework on the publicly available WS-DREAM datasets. The experimental results show the outperformance of our framework over the major state-of-the-art approaches.

A social network approach for recommending interoperable Web services

Modern application development leverages the invocation of a large pool of Web services such as Cloud services and APIs. As the number of Web services keeps growing, it becomes difficult for developers to identify services that can collaborate as part of the same composite application, or that can replace each other in failure cases. Gathering and analyzing Web services social interaction such as composition, substitution, and subscription helps building communities of interoperable services (i.e., likely to collaborate with each other and/or to replace each other). This paper proposes a new approach for recommending interoperable services to developers based on the multi-dimensional analysis of their social interaction history. The approach aims to build communities of services with highly dense interaction relationships. Services part of the same community are recommended to developers as potential collaborators or substitutes. The proposed approach identifies first service leaders. Leaders are particular services with a high interaction rate in the network around which communities are built. Remaining services followers join communities based on their previous interaction experiences. Followers leverage the votes of their experienced neighbors to make their final vote. Experiments on pseudo-real data show that leveraging services social interaction outperforms state-of-the-art approaches.

Performance Analysis of a Clustering Model for QoS-Aware Service Recommendation

The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods.

Multi-Intentional Description of Learning Semantic Web Services

E-learning systems use web service technology to develop distributed applications. Therefore, with the tremendous growth in the number of web services, finding the proper services while ensuring the independence and reusability of the learning objects in a different context has become an important issue and has attracted much interest. This article first proposes an extension of the Ontology Web Language for Services Learning Object (OWLS-LO) model to describe a multi-intentional learning object. This description ensures accessibility to learning objects. This research then presents a service discovery mechanism that uses the new semantic model for service matching. Experimental results show that the proposed semantic discovery mechanism using multi-intention model performs better than discovery mechanism based on single intention.

A probability distribution detection based hybrid ensemble QoS prediction approach

Recently, with the rapid increase in the number of web services, how to discover services to meet personalized requirements of users in large-scale scenarios has become a popular topic in both industry and academia. The quality of service (QoS) prediction-based web service recommendation system has been widely studied as an effective method for solving this problem. However, due to the sparsity and imbalance of the observed QoS data, existing QoS prediction approaches suffer from limited prediction accuracy and poor scalability. Moreover, existing QoS prediction approaches do not consider the probability distribution of the observed QoS data, which have remarkable impacts on the prediction performance. In this paper, we propose a novel probability distribution detection-based hybrid ensemble QoS prediction model that can dynamically integrate a set of basic prediction models to improve the prediction accuracy instead of designing complex and time-consuming models. Specifically, we first propose an enhanced CF (collaborative filtering)-based approach as the basis of the prediction model. Second, given the results of a set of other basic prediction models, in addition, we propose a distribution detection algorithm to calculate the PCWs (probability confidence weights) of those results. Finally, we combine them dynamically to obtain final results based on PCWs. Experiments based on real datasets show that our approach has higher prediction accuracy and better scalability than existing mainstream QoS prediction approaches.

Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation

Abstract The convergence of Social Mobility Analytics and Cloud (SMAC) technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of the non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turns out to be a challenging research issue. In the current work, various neural network models like Levenberg Marquardt (LM), Bayesian-Regularization (BR) and Scaled-Conjugate-Gradient (SCG) are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Pearson’s Correlation Coefficient is used as an evaluation metric and their value for the prediction of Response time is found to be 0.84985 and for throughput (TP) is found to be 0.99082 with the Levenberg Marquardt algorithm. Thus, the experimental results show that the with the Levenberg Marquardt model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time as well as throughput.

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays. However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios. For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete. For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance. Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC. The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization. Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems. Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches.

An Econometric Time Series Forecasting Framework for Web Services Recommendation

The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, various econometric models are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) value is used as an evaluation metric and their value for the prediction of Response time is found to be 1.7392e+04 and 1.7416e+04 respectively with the ARMA model. The AIC and BIC value for throughput (TP) is found to be 1.5249e+04 and 1.5334e+04 respectively with the ARIMA. The value of variance is 13.2437 and 6.8131 for RT and TP respectively which is also the lowest among other models with t-statistic greater than the p-value. Thus, the experimental results show that the ARMA and ARIMA model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time and throughput respectively.

Dynamic service composition in enterprise cloud bus architecture

Purpose Service composition phenomenon based on non-scenario aspects are become the latest issues in enterprise software applications of the multi-cloud environment due to the phenomenal increase in a number of Web services. The traditional service composition patterns are hard to support the dynamic, flexible and autonomous service composition in the inter-cloud platform. To address this problem, this paper aims to describe a dynamic service composition framework (SCF) that is enriched with various structural and functional aspects of composition patterns in a cloud computing environment. The proposed methodology helps to integrate various heterogeneous cloud services dynamically to acquire an optimal and novel enterprise solution for delivering the service to the end-users automatically. Design/methodology/approach SCF and different composition patterns have been used to compose the services present in the inter-cloud architecture of the multi-agent-based system. Further, the proposed dynamic service composition algorithm is illustrated using a hybrid approach, where service are chosen according to various needs of quality of service parameters. Besides, a priority-based service scheduling algorithm is proposed that facilitates the automation of delivering cloud service optimally. Findings The proposed framework is capable of composing the heterogeneous service and facilitate the structural and functional aspects of service composition process in enterprise cloud-based applications in terms of flexibility, scalability, integrity and dynamicity of the cloud bus. The advantage of the proposed algorithm is that it helps to minimize the execution cost, processing time and get better success rate in delivering the service as per customer’s need. Originality/value The novelty of the proposed architecture coordinates cloud participants, automate service discovery pattern, reconfigure scheduled services and focus on aggregating a composite services in inter-cloud environments. Besides, the proposed framework supported several non-functional characteristics such as robustness, flexibility, dynamicity, scalability and reliability of the system.

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.

Alleviating sparsity by specificity‐aware ontology‐based clustering for improving web service recommendation

The rapid development of the Internet in recent years has led to a vast increase in the numbers of Web services, which challenge users' capability to find their favorite services quickly and accurately. There is thus an urgent demand for service recommendations that help discover applicable services. Although the collaborative filtering technique is one of the most successful recommendation system technologies, it suffers from data sparsity and cold‐start problems, which in turn lead to inaccurate results. In this article, we deal with these issues by applying a novel ontology‐based clustering approach that uses domain specificity and service similarity for ontology generation. This clustering approach can easily and effectively increase the data density of the user‐service dataset by assuming blank user preferences according to the history of user‐favored domain(s). Then, user ratings are predicted by calculating the trust value between users. The experimental results indicate that the proposed approach can effectively alleviate the data sparsity and cold‐start problems with lower prediction error with the best recommendation performance, and our method performed better than baseline approaches in terms of accuracy and novelty. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> overing-based quality prediction method for Web services via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</b> eighborhood-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> atrix <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</b> actorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.

Система каталогизации и мониторинга территориально распределенных вычислительных узлов в среде WPS сервисов для решения геологических задач

The study describes an approach for heterogeneous web services cataloguing of geographically distributed computing nodes in the computational and analytical geological environment. The approach of creation of web services monitoring system is proposed. In this study the approach for web services catalogue organization based on SOAP (Simple Object Access Protocol) protocol, with use of UDDI (Universal Description Discovery and Integration) registries is reviewed. The scheme for heterogeneous web services description with an ability of further creation of catalogue is proposed. The catalogue includes functions for searching and receiving of metainformation about every web service in it. The study also describes an approach for computational and analytical geological environment creation. A number of web services for heterogeneous data processing used in geological research is described. The approaches for organization of cataloguing and web services monitoring provide a higher level of reliability of computational and analytical geological environment. They provide an information about web service reliability within environment.

Predicting new composition relations between web services via link analysis

With the wide application of service-oriented architecture (SOA) and service-oriented computing (SOC), the past decade has witnessed a rapid growth of the number of web services on the internet. Against this background, combining different web services to create new applications has attracted great interest from developers. However, according to latest statistics, only a small number of popular services are frequently used by developers and the use rates of most web services are rather low. To help service users discover appropriate web services and promote service compositions has thus become a significant need. In the following paper, we propose a link-based approach to predict new composition relations between web services. The approach is based on exploration of known composition relations and similarity relations among web services. To measure the composition or similarity degrees, several link-based methods are exploited, and two reasonable heuristic rules for integrating the existing composition and similarity relations for service composition prediction are developed. Case studies and experiments based on real web service datasets validated the proposed approach.