Web Service QoS Research Articles

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

Joint QoS Prediction for Web Services based on Deep Fusion of Features

In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).

Matrix Factorization-based Web Service QoS Prediction: Methods and Applications

Matrix Factorization-based Web Service QoS Prediction: Methods and Applications

Time-Aware Smart City Services Based on QoS Prediction: A Contrastive Learning Approach

Smart cities are designed to satisfy the needs of residents and improve their quality of life by providing a wide range of smart city services. The key to the efficient operation of smart city services is accurate forecast of the missing QoS (Quality of Service).. At present, many approaches utilize the context information of users and services, such as geographic location and network location, to somewhat increase the prediction accuracy and forecast the missing QoS values.. However, because the network conditions and server status are unpredictable, time is also considered as one of the important factors affecting QoS prediction, which brings more challenges as follows: higher data dimension, more complex data characteristics and higher data sparsity. To overcome these challenges,z we suggest an approach for time-aware Web service QoS prediction based on contrastive learning(named CLpred). CLpred utilizes a sequential data input format for QoS data and models these QoS sequences through Transformer encoder with contrastive learning framework. Therefore, it can downscale QoS data and extract a more efficient representation in complex QoS data. Furthermore, it makes it possible to apply data augmentation methods to address these probelms of data sparsity. In order to prove the superiority of the this approach, particularly inside the presence of extremely high data sparsity, extensive tests are carried out on the well-known service QoS data set WSDREAM.

A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data.

With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data. However, such data are naturally time dependent. For addressing this critical issue, this study presents a Kalman-filter-incorporated latent factor analysis (KLFA)-based QoS-estimator for accurate representation to temporally dynamic QoS data. Its main idea is to make the user latent features (LFs) time dependent, while the service ones time consistent. A novel iterative training scheme is designed, where the user LFs are learned through a Kalman filter for precisely modeling the temporal patterns, and the service ones are alternatively trained via an alternating least squares algorithm for precisely representing the historical QoS data. Empirical studies on large-scale and real Web service QoS datasets demonstrate that the proposed KLFA model significantly outperforms state-of-the-art QoS-estimators in estimation accuracy for dynamic QoS data.

A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction

Quality-of-Service (QoS), which describes the non-functional characteristics of Web service, is of great significance in service selection. Since users cannot invoke all services to obtain the corresponding QoS data, QoS prediction becomes a hot yet thorny issue. To date, a latent factor analysis (LFA)-based QoS predictor is one of the most successful and popular approaches to address this issue. However, current LFA-based QoS predictors are mostly modeled on inner product space with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm-oriented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Loss</i> function only. They cannot comprehensively represent the characteristics of target QoS data to make accurate predictions because inner product space and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm have their respective limitations. To address this issue, this study proposes a Double-space and Double-norm Ensembled Latent Factor (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E-LF) model. Its main idea is three-fold: 1) Double-space—inner product space and distance space are employed to model two kinds of LFA-based QoS predictors, respectively, 2) Double-norm—both of these two predictors adopt an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -and- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm-oriented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Loss</i> function, and 3) Ensembled—building an ensemble of these two predictors by a weighting strategy. By doing so, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E-LF integrates multi-merits originating from inner product space, distance space, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm, making it achieve highly accurate QoS prediction. Experiments on two real-world QoS datasets demonstrate that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E-LF has significantly higher prediction accuracy than state-of-the-art models.

On the development of the web service quality modelling space

Most web service (WS) based systems consider the quality of service (QoS) to select an appropriate WS to run an application. Some authors have proposed QoS attributes that characterize WS at the technological layer to determine QoS. However, this is not enough to meet user needs and ensure user satisfaction at the domain layer. Consequently, there is a need to develop a deeper understanding of the user Quality of Experience (QoE) for WS. This paper proposes a QoE modelling space, which views and allows predicting QoE through QoS attributes, WS architecture layers, and stakeholder viewpoints from a particular perspective. The proposed WS quality modelling space was validated using the objective (the WS-DREAM dataset), and subjective (expert’s judgments) attributes to estimate QoS performance by ARAS and to predict QoE performance by a Fuzzy Control System (FCS). The obtained results show the strong positive linear and strong positive monotonic relationships between WS QoS performance and WS QoE performance. Therefore, the proposed WS quality modelling space is suitable for QoE performance prediction based on measurable QoS attributes.

Web Service QoS Prediction via Collaborative Filtering: A Survey

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.

Robustness Estimation and Optimisation for Semantic Web Service Composition With Stochastic Service Failures

Service-oriented architecture (SOA) is a widely adopted software engineering paradigm that encourages modular and reusable applications. One popular application of SOA is web service composition, which aims to loosely couple web services to accommodate complex goals not achievable through any individual web service. Many approaches have been proposed to construct composite services with optimized Quality of Service (QoS), assuming that QoS of web services never changes. However, the constructed composite services may not perform well and may not be executable later due to its component services' failure. Therefore, it is important to build composite services that are robust to stochastic service failures. Two challenges of building robust composite services are to efficiently generate service composition with near-optimal quality in a large search space of available services and to accurately measure the robustness of composite services considering all possible failure scenarios. This article proposes a novel two-stage GA-based approach to robust web service composition with an adaptive evolutionary control and an efficient robustness measurement. This approach can generate robust composite service at the design phase, which can cope with stochastic service failures and maintain high quality at the time of execution. We have conducted experiments with benchmark datasets to evaluate the performance of our proposed approach. Our experiments show that our method can produce highly robust composite services, achieving outstanding performance consistently in the event of stochastic service failures, on service repositories with varying sizes.

An expanded Matrix Factorization model for real-time Web service QoS prediction

An expanded Matrix Factorization model for real-time Web service QoS prediction

A Location-Based Factorization Machine Model for Web Service QoS Prediction

With the prevalence of web services, a large number of similar web services are provided by different providers. To select the optimal service among these service candidates, Quality of Service (QoS), representing the non-functional characteristics, plays an important role. To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. Collaborative web service QoS prediction is one of the most popular approaches. Based on the historical QoS data, collaborative QoS prediction methods employ memory-based collaborative filtering (CF), model-based CF, or their hybrids to predict QoS values. However, these methods usually only consider the QoS information of similar users and services, neglecting the correlation between them. To enhance the prediction accuracy, we propose a novel method to predict QoS values based on factorization machine, which leverages not only QoS information of users and services but also the user and service neighbor’s information. To evaluate our approach, we conduct experiments on a large-scale real-world dataset with 1,974,675 web service invocations. The experiment results show that our approach achieves higher prediction accuracy than other QoS prediction methods.

Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem

Many web-based software systems have been developed in the form of composite services. It is important to accurately predict the Quality of Service (QoS) value of atomic web services because the performance of such composite services depends greatly on the performance of the atomic web service adopted. In recent years, collaborative filtering based methods for predicting the web service QoS values have been proposed. However, they are mainly faced with a cold start problem that is difficult to make reliable prediction due to highly sparse historical data, newly introduced users and web services, and the existing work only deals with the case of newly introduced users. In this article, we propose a Location-based Matrix Factorization using a Preference Propagation method (LMF-PP) to address the cold start problem. LMF-PP fuses invocation and neighborhood similarity, and then the fused similarity is utilized by preference propagation. LMF-PP is compared with existing approaches on the real world dataset. Based on the experimental results, LMF-PP shows better performance than existing approaches in cold start environments as well as in warm start environments.

Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

Optimal web service composition using hybrid optimization algorithm in cloud environment

In recent years, service-based applications are deemed to be one of the new solutions to build an enterpriseapplication system. In order to answer the most demandingneeds or adaptations to the needs of changed servicesquickly, service composition is currently used to exploitthe multi-service capabilities in the Information Technologyorganizations. While web services, which have been independently developed, may not always be compatible with each other, the selection of optimal services and compositionof these services are seen as a challenging issue. In this paper we propose optimal web service composition (OWSC) model, we introduce an improved Dragonfly Algorithm (IDA) to compute services whose network positions are closer to each other and to the users, which ensures the QoS of web service and network. The rule search swarm searcher (RSSA) is used to obtain the desirable characteristics from composite web services for further optimal selection of service composition set in composite planner. Then, we propose optimal tree based replanning mechanism to adapt the execution plan to the actual behavior of already executed services by a dynamic service selection at runtime.Finally, the proposed OWSC model is going to develop by cloudsim toolkit and the performance is compared with the existing models similar to our contributions in terms of QoS parameters.

МОДЕЛЬ ІЄРАРХІЇ КРИТЕРІЇВ ЯКОСТІ ВЕБСЕРВІСІВ

Технології інтернет-сервісів пропонують передові рішення для створення розподілених бізнес-процесів та додатків. У наш час кількість інтернет- сервісів продовжує постійно зростати. Перед споживачами інтернет- сервісів постає проблема вибору із сервісів з однаковою або подібною функціональністю саме того сервісу, який їм найбільше підходить за набором критеріїв, наприклад, вартість, час відгуку, пропускна здатність, безпека, надійність. Такі критерії якості вебсервісів називають нефункціональними характеристиками сервісів або якістю сервісу (Quality of Service, QoS). Залежно від домену та категорій користувачів, а також контексту використання сервісу вебсервіс може мати багато специфічних властивостей та характеристик якості. Є багато стандартів та специфікацій якості вебсервісів. Незважаючи на це, дослідники, розробники та замовники часто по-різному розуміють якість вебсервісів. Вони можуть зменшити або розширити перелік і навіть значення характеристик якості вебсервісів. Метою цієї роботи є розроблення моделі критеріїв якості вебсервісів, яка могла б ураховувати різні аспекти впливу на якість вебсервісів. У цій роботі розглянуто специфікації та стандарти таких організацій, як OASIS, ISO/IEC та OMG. Здійснено пошук інформації у наукометричних базах даних, таких як Scopus та Web of Science. Із розглянутих стандартів та відповідних наукових робіт обрано характеристики якості та критерії для розроблення ієрархічних моделей якості. На основі стандарту метамоделі QoS від Object Management Group запропоновано модель для синтезу колекцій характеристик вебсервісу QoS. У результаті дослідження розроблено модель, засновану на аналізі наявних стандартів, наукових досліджень та оглядів, присвячених вивченню та класифікації характеристик якості та атрибутів якості вебсервісів. Ця модель може бути корисною для вибору вебсервісу з метою безпосереднього застосування, побудови композитних вебсервісів та хмарних сервісів, створення систем на основі сервіс-орієнтованої архітектури та інтернету речей.

Qos Web Service Design Based on Collaborative Filtering

In the process of web service recommendation, the prediction accuracy of Web Service missing Quality of Service (QoS) value will have an important impact on the rationality of service recommendation. Therefore, combined with spatiotemporal similarity perception, this paper proposes a new web service QoS collaborative filtering recommendation algorithm. This paper designs the framework of web service recommendation system from the perspective of QoS collaborative prediction, and gives the definition of related parameter set. Aiming at the problem that some services in the traditional Top-k algorithm are not similar to the target services, the spatial-temporal similarity perception combined with similar weight is used to predict the missing data to improve the prediction accuracy. In this paper, the calculation process of the algorithm is given through a simple example. The effectiveness of the algorithm is verified by the experimental results.

Req-WSComposer: a novel platform for requirements-driven composition of semantic web services

Service-Oriented Computing (SOC) describes a specific paradigm of computing that utilizes Web services as reusable components in order to develop new software applications. SOC allows distributed applications to work together via the Internet without direct human intervention. In this work, we propose a new SOC-based approach to ensure application development. This approach ensures the discovery, selection, and composition of the most appropriate Web services. With this approach, various requirements (both functional and non-functional) are specified by the developer to satisfy QoS, QoE, and QoBiz parameters and Web services are selected and composed to meet these requirements. Our approach is implemented using the Req-WSComposer (Requirements-based Web Services Composer) platform, whose functionalities are tested using an extended and enriched version of the OWLS-TC dataset, which includes around 10,830 semantic Web services descriptions. The results of our experiments demonstrate that the proposed approach enables users to extract the most appropriate composition solution that satisfies the developer's pre-determined requirements.

QoS Time Series Modeling and Forecasting for Web Services: A Comprehensive Survey

In research, time series-based (time-aware) QoS modeling and forecasting for Web services (WSs) has been studied for over a decade, and a large number of research papers have been published; however, this current research lacks a systematic and comprehensive survey. Thus, in this article, an insightful and detailed review and investigation of current WS QoS time series modeling and forecasting research is provided. Based on our investigation of the collected literature, we identify and divide the entire research subject into four essential research concerns (components): the addressed problem, the proposed or employed approach, the considered performance measure, and the adopted QoS time series dataset. This article reviews, classifies, and discusses current studies in terms of the four identified research concerns. Moreover, for each research concern, a set of criteria and classification are proposed for its detailed categorization and comparison. Finally, this survey also discusses the insufficiency of current studies in each research concern and the potential future directions of this research area. Overall, this article can be an informative guide for researchers to understand the concerns that they should address when studying this research subject and to clearly comprehend what has been done in current research for each identified research concern.

A deep learning approach for collaborative prediction of Web service QoS

Web services are a cornerstone of many crucial domains, including cloud computing and the Internet of Things. In this context, QoS prediction for Web services is a highly important and challenging issue that facilitates the building of value-added processes such as compositions and workflows of services. Current QoS prediction approaches, like collaborative filtering methods, mainly suffer from obstacles related to data sparsity and the cold-start problem. Moreover, previous studies have not conducted in-depth explorations of the impact of the geographical characteristics of services/users and QoS ratings on the prediction problem. To address these difficulties, we propose a deep-learning-based approach for QoS prediction. The main idea consists of combining a matrix factorization model based on a deep autoencoder (DAE) with a clustering technique based on geographical characteristics to improve the effectiveness of prediction. The overall method proceeds as follows: first, we cluster the input QoS data using a self-organizing map that incorporates knowledge of geographical neighborhoods; by doing so, we can reduce the data sparsity while preserving the topology of the input data. In addition, the clustering step effectively overcomes the cold-start problem. Next, we train a DAE that minimizes the squared loss between the ground-truth QoS and the predicted one, for each cluster. Finally, the unknown QoS of a new service is predicted using the trained DAE related to the closest cluster. To evaluate the effectiveness and robustness of our approach, we conducted a comprehensive set of experiments based on a real-world Web service QoS dataset. The experimental results show that our method achieves better prediction performance than several state-of-the-art methods.

An LSTM Based Time Series Forecasting Framework for Web Services Recommendation

The convergence of Social Mobility Analytics and Cloud (SMAC) technologies gives rise to an unforeseen aggrandization of the web services on the internet. The resilience and payment-based approach of the cloud makes it an obvious choice for the deployment of web services-based applications. Out of available web services, to gratify the similar functionalities, the choice of the web service based on the personalized quality of service (QoS) parameters plays an importantrole in determining 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 non-functional performance of web services correlated behavior with the invocation time. This happens because service status usually changes over time. Hence, the design of the time a ware web service recommendation system based on the personalized QoS parameters is very crucial and becomes a challenging research issue. In this study, LSTM based deep learning models were used for the prediction of these time aware QoS parameters and the results are compared with the previous approaches. The experimental results show that the LSTM based Time Series Forecasting Framework is performing better. The RMSE, MAE, and MAPE are used as an evaluation metric and their value for the prediction of Response time (RT) is found to be 0.030269, 0.02382 and 0.59773 respectively with adaptive moment estimation as the training option and is found to be 0.66988, 0.66465 and 27.9934 respectively with root mean square propagation as the training option. The RMSE, MAE, and MAPE value for the prediction of through put (TP) is found to be 0.77787, 0.4792 and 1.61 respectively with adaptive moment estimation as the training option and is found to be 0.2.7087, 1.4076 and 7.1559 respectively with root mean square propagation as the training option respectively. Thus, the experimental results show that the LSTM model of Time Series Fore casting for Web Services Recommendation Framework is performing better as compared to previous methods.