QoS Prediction Method Research Articles

EMD-EmLSTM: A QoS Analysis and Prediction Method for Industrial Internet of Things

EMD-EmLSTM: A QoS Analysis and Prediction Method for Industrial Internet of Things

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).

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.

QoS prediction for service selection and recommendation with a deep latent features autoencoder

The number of services on the Internet has increased rapidly in recent years. This makes it increasingly difficult for users to find the right services from a large number of the functionally equivalent candidate. In many cases, the number of services invoked by a user is quite limited, resulting in a large number of missing QoS values and sparseness of data. Consequently, predicting QoS values of the services is important for users to find the exact service among many functionally similar services. However, improving the accuracy of QoS prediction is still a problem. Despite the successful results of the proposed QoS prediction methods, there are still a set of issues that should be addressed, such as Sparsity and Overfitting. To address these issues and improve prediction accuracy. In this paper, we propose a novel framework for predicting QoS values and reduce prediction error. This framework named auto-encoder for neighbor features (Auto-NF) consists of three steps. In the first step, we propose an extended similarity computation method based on Euclidean distance to compute the similarity between users and find similar neighbors. In the second step, we form clusters of similar neighbors and partition the initial matrix into sub-matrices based on these clusters to reduce the data sparsity problem. In the third step, we propose a simple neural network autoencoder that can learn deep features and select an ideal number of latent factors to reduce the overfitting phenomenon. To validate and evaluate our method, we conduct a series of experiments use a real QoS dataset with different data densities. The experimental results demonstrate that our method achieves higher prediction accuracy compared to existing methods.

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.

Service recommendation based on parallel graph computing

With the development of cloud service technologies, the amount of services grows rapidly, leading to building high-quality services an urgent and crucial research problem. Service users should evaluate QoS to select the optimal cloud services from a series of functionally equivalent service candidates, because QoS performance of services is varying over time. The reason is that QoS is related to the service overload and network environments. This phenomenon makes QoS prediction for users located in different places even harder. Furthermore, since service invocations are charged by service providers, it is impractical to let users invoke required cloud services to evaluate quality with respect to time and resources. To solve this problem, this paper proposes a cloud service QoS prediction method, called TPP (Time-aware and Parallel Prediction), to provide time-aware and parallel QoS value prediction for various service users. TPP is able to predict without additional invocation of cloud services, since it uses past cloud service usage experience from different service users. We propose and implement tensor decomposition algorithm on the Spark system. The results of extensive experimental show the accuracy and efficiency of TPP.

The QoS Prediction of Web Service with Location Information Ensemble

With the increasing presence and adoption of web services, accurate QoS prediction methods are becoming increasingly important. Although some QoS prediction techniques have been proposed and analyzed recently, the performance is not satisfactory, since they didn't take the relation between QoS values and users' physical locations into consideration. In order to improve the precision of web service QoS prediction, we propose a probabilistic matrix factor model that fuses the users' own properties and their physical neighbors' performance together, so as to make a comprehensive use of both the users' QoS records and their location relation. The experimental results show that our method performs better than the state-of-the-art approaches.

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.

A Sequential pattern based QoS prediction method for web service

A Sequential pattern based QoS prediction method for web service