QoS Prediction Approach 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.

A context-aware factorisation machine approach for accurate QoS prediction

A context-aware factorisation machine approach for accurate QoS prediction

A context-aware factorisation machine approach for accurate QoS prediction

A context-aware factorisation machine approach for accurate QoS prediction

Toward Effective Personalized Service QoS Prediction From the Perspective of Multi-Task Learning

End-to-end QoS measurement plays an indispensable role in the decision-making of cloud services and IoT services. Many efforts have paid on developing QoS prediction approaches in the past decade leveraging the principle of collaborative filtering. But there remain many challenging issues concerning multi-task prediction requirements, feature selection for heterogeneous prediction tasks, and model training. To this end, we propose an effective personalized service QoS prediction method from the perspective of multi-task learning, named PMT. PMT consists of specially-designed feature selection components and a multi-step model training strategy. The feature selection method leverages the principle of multi-expert decision-making and self-attention mechanism. The multi-step model training enables a weight-free configuration for parallel prediction tasks. Experimental results on a large dataset with two tasks and a small dataset with three tasks demonstrate that PMT is superior to the state-of-the-art QoS prediction methods.

A Missing QoS Prediction Approach via Time-Aware Collaborative Filtering

Quality of Service (QoS) guarantee is an important issue in building service-oriented applications. Generally, some <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> values of a service are unknown to its users who have never invoked the service before. Fortunately, collaborative filtering ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CF</i> )-based methods are proved feasible for missing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> prediction and have been widely used. However, these methods seldom took the temporal factors into consideration. Indeed, historical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> values contain more information about user (or service) similarity. Furthermore, as the application environment is dynamic, obtained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> values usually have short timeliness. Hence, using outdated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> values will largely decrease the prediction accuracy. In order to resolve this issue, we proposed a time-aware collaborative filtering approach. First, we proposed a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> model to filter out outdated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> values, and divided the obtained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> values into several time slices. Then, we computed the average value of historical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> as temporal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> forecast. In addition, by introducing time-aware similarity computation mechanism, we succeeded to select real similar neighbor users (or services) and further predict the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CF</i> -based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CF</i> technology. Finally, we can predict the final missing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> by combining temporal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> forecast and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CF</i> -based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QoS</i> prediction. Experiment results show that our approach can receive better prediction precision.

A hybrid tensor factorization approach for QoS prediction in time-aware mobile edge computing

A hybrid tensor factorization approach for QoS prediction in time-aware mobile edge computing

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

In recent years, the number of online services has grown rapidly, invoking the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction (PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first uses the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. At the density of 5%–30%, the experimental results on both QoS attribute response time and throughput indicate that PLRes performs better than the existing five state-of-the-art QoS prediction approaches.

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.

Long-Term IaaS Selection Using Performance Discovery

We propose a novel framework to select IaaS providers according to a consumer’s long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider’s long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.

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.

A location-aware matrix factorisation approach for collaborative web service QoS prediction

Predicting the unknown QoS is often required due to the fact that most users would have invoked only a small fraction of web services. Previous prediction methods benefit from mining neighbourhood interest from explicit user QoS ratings. However, the implicitly existing but significant location information that would potentially tackle the data sparsity problem is overlooked. In this paper, we propose a unified matrix factorisation model that fully capitalises on the advantages of both location-aware neighbourhood and latent factor approach. We first develop a multiview-based neighbourhood selection method that clusters neighbours from the views of both geographical distance and rating similarity relationships. Then a personalised prediction model is built up by transforming the wisdom of neighbourhoods. Experimental results have demonstrated that our method can achieve higher prediction accuracy than other competitive approaches and as well as better alleviating the data sparsity issue.

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

service level agreement (SLA) management is one of the key issues in cloud computing. The primary goal of a service provider is to minimize the risk of service violations, as these results in penalties in terms of both money and a decrease in trustworthiness. To avoid SLA violations, the service provider needs to predict the likelihood of violation for each SLO and its measurable characteristics (QoS parameters) and take immediate action to avoid violations occurring. There are several approaches discussed in the literature to predict service violation; however, none of these explores how a change in control parameters and the freshness of data impact prediction accuracy and result in the effective management of an SLA of the cloud service provider. The contribution of this paper is two-fold. First, we analyzed the accuracy of six widely used prediction algorithms—simple exponential smoothing, simple moving average, weighted moving average, Holt–Winter double exponential smoothing, extrapolation, and the autoregressive integrated moving average—by varying their individual control parameters. Each of the approaches is compared to 10 different datasets at different time intervals between 5 min and 4 weeks. Second, we analyzed the prediction accuracy of the simple exponential smoothing method by considering the freshness of a data; i.e., how the accuracy varies in the initial time period of prediction compared to later ones. To achieve this, we divided the cloud QoS dataset into sets of input values that range from 100 to 500 intervals in sets of 1–100, 1–200, 1–300, 1–400, and 1–500. From the analysis, we observed that different prediction methods behave differently based on the control parameter and the nature of the dataset. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results to manage SLA intelligently and avoid violation penalties.

Differential private collaborative Web services QoS prediction

Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user’s observed QoS values by applying differential privacy to the user’s QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user’s QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.

A Novel QoS Prediction Approach for Cloud Services Using Bayesian Network Model

Cloud computing is the next generation computing model, which has a significant position in the field of scientific and business computing. By predicting cloud service’s QoS in next period, it is helpful for end users to choose the most suitable cloud service that meets their needs. The underlying hardware/software resources of cloud architecture may have a certain influence on cloud service QoS. However, existing cloud service QoS prediction approaches do not take this influence into account. As these effects are real during the process of cloud service QoS prediction, ignoring the impact of these effects may create a big gap between the prediction results and the actual results. Therefore, in this paper interactive information is first used to describe the correlation between the hardware/software resources and the QoS attributes of the cloud service. Then, a Bayesian network model is established to predict cloud QoS. Bayesian network prediction reasoning algorithm is used to predict and reason about the future QoS values. A set of dedicated experiments is conducted to validate that our approach can accurately predict QoS of cloud service and the accuracy rate is better than state-of-the-art approaches.

A Referral-Based QoS Prediction Approach for Service- Based Systems

A Referral-Based QoS Prediction Approach for Service- Based Systems

Clustering-based uncertain QoS prediction of web services via collaborative filtering

Although collaborative filtering (CF) has been widely applied for QoS-aware web service recommendation, most of these approaches mainly focus on certain QoS prediction. However, they failed to take the natural characteristic of web services with QoS uncertainty into account in service-oriented web applications. To solve the problem, this paper proposes a novel approach for uncertain QoS prediction via collaborative filtering and service clustering. We first establish uncertain QoS model for a service user, where each service is formalised as a QoS matrix. To mine the similar neighbourhood users for an active user, we then extend the Euclidean distance to calculate the similarity between two uncertain QoS models. Finally, we present two kinds of QoS prediction strategies based on collaborative filtering and clustering, called U-Rec and UC-Rec. Extensive experiments have been carried on 1.5 million real-world uncertain QoS transaction logs of web services. The experimental results validate the effectiveness of our proposed approach.

QoS prediction for Web service in Mobile Internet environment

ABSTRACTQuality of Services (QoS) prediction plays an important role in Web service recommendation. Many existing Web service QoS prediction approaches are highly accurate and useful in Internet environments. However, the QoS data of Web service in Mobile Internet are notably more volatile, which makes these approaches fail in making accurate QoS predictions of Web services. In this paper, by weakening the volatility of QoS data, we propose an accurate Web service QoS prediction approach based on the collaborative filtering algorithm. This approach contains three processes, that is, QoS preprocessing, user similarity computing and QoS predicting. We have implemented our proposed approach with an experiment based on real-world and synthetic datasets. The results demonstrate that our approach outperforms other approaches in Mobile Internet.

A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation

Due to the popularity of service-oriented architectures for various distributed systems, an increasing number of Web services have been deployed all over the world. Recently, Web service recommendation became a hot research topic, one that aims to accurately predict the quality of functional satisfactory services for each end user. Generally, the performance of Web service changes over time due to variations of service status and network conditions. Instead of employing the conventional temporal models, we propose a novel spatial-temporal QoS prediction approach for time-aware Web service recommendation, where a sparse representation is employed to model QoS variations. Specifically, we make a zero-mean Laplace prior distribution assumption on the residuals of the QoS prediction, which corresponds to a Lasso regression problem. To effectively select the nearest neighbor for the sparse representation of temporal QoS values, the geo-location of web service is employed to reduce searching range while improving prediction accuracy. The extensive experimental results demonstrate that the proposed approach outperforms state-of-art methods with more than 10% improvement on the accuracy of temporal QoS prediction for time-aware Web service recommendation.

Towards QoS Prediction for Web Services based on Adjusted Euclidean Distances

QoS-based service selection is an important issue of service-oriented computing (SOC). A common premise of previous research is that the QoS values of services to target users are supposed all known. However, the real situation is that many of them may be missing. In this paper, we propose an enhanced CF-based QoS prediction approach to predict such missing values. Compared with existing QoS prediction methods, our proposed one has three new features: 1) adding a data normalization process to remove the impact of different QoS scale; 2) using the adjusted Euclidean Distances equation for similarity calculation to improve the prediction accuracy; and 3) using a fusion approach to predict the missing values from two sources(user and service based). An extensive performance study based on a real public dataset is reported to verify its effectiveness.

QoS Prediction Approach for Web Service Recommendation

This paper proposes a QoS prediction approach used to recommend web service to solve the problem of choosing high quality web service. This approach bases on other users’ experience. According to the different feeling of different user to the same service, and the different similarity among the QoS of different services, the approach first divides the users and services into several modes, and then computes the means of one mode to predict the QoS of a service that the user never used before. Experiment simulation show that the approach is operable to some extent.