Prediction Of Quality Of Service Values Research Articles

BGCL: Bi-subgraph network based on graph contrastive learning for cold-start QoS prediction

With the advent of Web service technologies, the number of services published on the cloud is increasing rapidly. The quality of service (QoS) becomes a crucial criterion for selecting services from a massive pool of candidates. Collaborative filtering (CF) has become a major way for personalized QoS prediction by leveraging historical interactions between users and services. Due to the increasing number of users and services, CF-based QoS prediction often suffers from data sparsity and cold-start difficulties. Inspired by the advantages of graph contrastive learning in cold-start predictions, we propose BGCL, a bi-subgraph network based on graph contrastive learning to solve the above problems. Firstly, we generate different perspectives of user-neighborhood and service-neighborhood sub-graphs based on sparse user–service bipartite graphs. Next, our model learns user and service embeddings using the graph contrastive learning and graph attention aggregation mechanisms on the generated sub-graphs. Finally, user and service embeddings are fed into a multi-layer perception to predict QoS values. Experimental results show that our model outperforms several existing models in terms of prediction accuracy.

A Granular SVM-Based Method for Top-N Web Services Recommendation

With the wide application of service technologies in various fields, the number of services is increasing dramatically. So, a function is often provided by many services of different QoS (quality of service). Thus, due to the lack of professional knowledge, users often face great difficulties in finding the right services they really need. Hence, accurate and efficient service recommendation is not just an effective way of service advertising, but also an important means to promote user experience. Most of traditional service recommendation methods are based on predictions of QoS values. However, because of the dynamic nature of the Internet, it is hard to guarantee the predicted values are consistent with the actual values. This article proposes a granule distribution-aware SVM (support vector machine) model for service recommendation, namely GDSVM4SR. It takes advantages of granular computing to identify similar users, refine the training service set, and decrease the influence of unfaithful ratings. And then, GDSVM4SR trains an SVM separating hyperplane to sort unknown services and generate recommendations, thus it can avoid the prediction of QoS values. Experimental results show that the proposed GDSVM4SR outperforms several state-of-the-art methods in terms of the efficiency and the precision of recommendation.

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.

A QoS Prediction Approach Based on Fusion of Network Representation Learning and Dynamic Collaborative Filtering for Cloud Service

In recent years, the number of cloud services has increased dramatically with the development of internet technology. The recommendation of cloud service based on quality of service (QoS) has received more and more attention. The traditional matrix decomposition model has cold start and matrix sparse problems, which cannot fully describe the relationship between users and services, thus affecting the effect of QoS value prediction. In order to accurately predict the QoS value of missing cloud services, this paper proposes a prediction method of fusion network representation learning and dynamic collaborative filtering. The method can better mine the high-dimensional relationship vector of the user-service-attribute ternary network feature through network representation learning. The high-dimensional relation vector learned by the network representation is integrated into the dynamic collaborative filtering algorithm to solve the problem of sparse QoS matrix data in the dynamic collaborative filtering method. The effectiveness of the proposed method is verified by experimental simulation.

A QoS prediction approach based on fusion of network representation learning and dynamic collaborative filtering for cloud service

In recent years, the number of cloud services has increased dramatically with the development of internet technology. The recommendation of cloud service based on quality of service (QoS) has received more and more attention. The traditional matrix decomposition model has cold start and matrix sparse problems, which cannot fully describe the relationship between users and services, thus affecting the effect of QoS value prediction. In order to accurately predict the QoS value of missing cloud services, this paper proposes a prediction method of fusion network representation learning and dynamic collaborative filtering. The method can better mine the high-dimensional relationship vector of the user-service-attribute ternary network feature through network representation learning. The high-dimensional relation vector learned by the network representation is integrated into the dynamic collaborative filtering algorithm to solve the problem of sparse QoS matrix data in the dynamic collaborative filtering method. The effectiveness of the proposed method is verified by experimental simulation.

A Survey on Web Service QoS Prediction Methods

Nowadays, there are many Web services with similar functionality on the Internet. Users consider Quality of Service (QoS) of the services to select the best service from among them. The prediction of QoS values of the Web services and recommendations of the best service based on these values to the users is one of the major challenges in the web service area. Major studies in this field use collaboration filtering based methods for prediction. The paper introduced prediction methods and divided them into three main categories: memory-based methods, model-based methods, and Collaborative Filtering (CF) methods combined with other methods. In each category, some of the most famous studies were introduced, and then the problems and benefits of each category were reviewed. Finally, we have a discussion about these methods and propose suggestions for future works.

Context-Aware QoS Prediction With Neural Collaborative Filtering for Internet-of-Things Services

With the prevalent application of Internet of Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for Quality of Service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study the QoS prediction in IoT environments, where contextual information is vital. In this article, we develop a holistic framework to attack the QoS prediction in the IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world data sets, and the experimental results verify the effectiveness of the proposed framework.

IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments

Quality of Service (QoS) value prediction and QoS ranking prediction have their significance in optimal service selection and service composition problems. QoS based service ranking prediction is an NP-Complete problem which examines the order of ranked service sequence with respect to the unique QoS requirements. To address the NP-Complete problem, greedy and optimization-based strategies such as CloudRank and PSO have been widely employed in service oriented environments. However, they pose several challenges with respect to the similarity measure based QoS prediction, trap at local optima, and near optimal solution. Hence, this paper presents Improved Binary Gravitational Search Strategy (IBGSS), an optimization based search strategy to address the challenges in the state-of-the-art QoS value prediction and service ranking prediction techniques. IBGSS employs improved cosine similarity measure, and Newton–Raphson inspired Binary Gravitational Search Algorithm (NR-BGSA) for accurate QoS value prediction and optimal service ranking prediction respectively. The effectiveness of IBGSS over the state-of-the-art QoS value prediction and ranking prediction techniques was validated using two real world QoS datasets, namely WSDream#1 and web service QoS dataset in terms of various statistical measures (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Average Precision Correlation (APC)).

From similarity perspective: a robust collaborative filtering approach for service recommendations

Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions.We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.

Predicting Quality of Service via Leveraging Location Information

QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location‐aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user‐perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real‐world dataset to validate the effectiveness of the LANFM model.

Skyline service selection approach based on QoS prediction

The internet currently hosts a large number of web services with highly volatile quality of service (QoS), which makes it difficult for users to quickly access highly reliable online services. Hence, the selection of the optimal service composition based on fast and reliable QoS has emerged as a challenging and popular problem in the field of service computing. In this paper, we propose a service selection approach based on QoS prediction. We consider historical QoS information as time series and predict QoS values using the autoregressive integrated moving average model, which can provide more accurate QoS attribute values. We then calculate the uncertainty in the prediction results using an improved coefficient of variation to prune redundant services. In order to downsize the search space, we employ Skyline computing to prune redundant services and perform Skyline service selection using 0-1 mixed-integer programming. Experimental results based on real-world dataset showed that our approach yields satisfactory performance in terms of reliability and efficiency.

Skyline service selection approach based on QoS prediction

The internet currently hosts a large number of web services with highly volatile quality of service (QoS), which makes it difficult for users to quickly access highly reliable online services. Hence, the selection of the optimal service composition based on fast and reliable QoS has emerged as a challenging and popular problem in the field of service computing. In this paper, we propose a service selection approach based on QoS prediction. We consider historical QoS information as time series and predict QoS values using the autoregressive integrated moving average model, which can provide more accurate QoS attribute values. We then calculate the uncertainty in the prediction results using an improved coefficient of variation to prune redundant services. In order to downsize the search space, we employ Skyline computing to prune redundant services and perform Skyline service selection using 0-1 mixed-integer programming. Experimental results based on real-world dataset showed that our approach yields satisfactory performance in terms of reliability and efficiency.

A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values

Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.

Two-stage approach for reliable dynamic Web service composition

Web service composition is a key technology for creating value-added services by integrating available services. With the rapid development of Service Computing, Cloud Computing, Big Data, and the Internet of Things, mass services with the same functionalities and different quality of service (QoS) values are available on the Internet. Moreover, due to the uncertainties of services’ application environment, a service’s QoS is highly dynamic; these two factors cause reliable dynamic Web service composition to be a challenge. To address this issue, this paper proposes a two-stage approach for reliable dynamic Web service composition. In the first stage, the top K Web service composition schemes based on each service’s historical QoS values are selected with the proposed algorithm named Culture Genetic Algorithm (CGA). Then, component services in the top K schemes are filtered out and employed as the candidate services for dynamic service composition. This operation can greatly reduce the number of available services and filter out better candidate services for dynamic service composition. Next, the global QoS constraints are decomposed into local QoS constraints with the CGA algorithm, and the global optimal problem of service composition is transformed into a local optimal service selection problem; this conversion increases the flexibility of dynamic service composition and provides a chance to predict QoS values of services before service selection. In the second stage, before selecting the best service for each task during the running of the service composition workflow, QoS values of each candidate service are predicted based on the improved case-based reasoning, and the best service is selected according to the predicted QoS values. Through QoS prediction, the reliability of the composite Web service can be greatly enhanced. Finally, experimental results show that the proposed method is feasible and effective.

Service Selection for Web Services with Probabilistic QoS

Web services can be specified from two perspectives, namely functional and non-functional properties. Multiple services may possess the same function while vary in their non-functional properties, or called quality-of-service (QoS). QoS values are important criteria for service selection or recommendation. Most of the former works in web service selection and recommendation treat the QoS values as constants. However, QoS values of a service as perceived by a given user are intrinsically random variables because QoS value prediction can never be precise and there are always some unobserved random effects. In this work, we address the service selection problem by representing services' QoS values as discrete random variables with probability mass functions. The goal is to select a set of atomic services for composing a composite service such that the probability of satisfying constraints imposed on the composite service is high and the execution time is reasonable. Our proposed method starts with an initial web service assignment and incrementally adjusts it using simulated annealing. We conduct several experiments and the results show that our approach generally performs better than previous works, such as the integer programming method and the cost-driven method.

Topic Model based Collaborative QoS Prediction

With the increasing development and growth of Web services on the World Wide Web, the demand of appropriate Web service selection approaches are unprecedentedly strong, and Quality-of-Service (QoS) based service computing is becoming an important issue of service-oriented computing. In most of previous works, the QoS values of services to users are all conceived to be known, however, lots of them are unknown in practice application. Recently, lots of literatures aiming at predicting such missing QoS values are published, they all consider the unknown QoS values prediction as a fundamental step for the QoS-based service computing. Looking through existing works, we discover that the online cold-start scenario, in which some new coming Web services haven't been involved even once, hasn't been considered carefu lly. In this paper, we utilize a collaborative framework by integrating matrix factorization with probabilistic topic model to predict QoS values. Spec ifically, the basic idea of the proposed approach is collaborative filtering via matrix factorization, while the cold-start problem is handled by employing probabilistic topic model based on WSDL (Web Service Description Language) documents. The experiment are based on two real-world datasets (one contains 100 users and 150 Web services, and the other contains 339 users and 2344 Web services), and the results demonstrate the prediction accuracy of the proposed approach.

Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

With the increasing presence and adoption of web services on the World Wide Web, the demand of efficient web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming web service invocations, this paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantages of the past web service usage experiences of service users. We first apply the concept of user-collaboration for the web service QoS information sharing. Then, based on the collected QoS data, a neighborhood-integrated approach is designed for personalized web service QoS value prediction. To validate our approach, large-scale real-world experiments are conducted, which include 1,974,675 web service invocations from 339 service users on 5,825 real-world web services. The comprehensive experimental studies show that our proposed approach achieves higher prediction accuracy than other approaches. The public release of our web service QoS data set provides valuable real-world data for future research.

QoS-Aware Web Service Recommendation by Collaborative Filtering

With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.