Data Quality Of Service Research Articles

StegoDCF: A New Covert Channel for Smart Grids Utilizing the Channel Access Procedure in Wi-Fi Networks

Wi-Fi networks within the smart grid play a vital role in enabling communication between smart meters and data collectors. They are also frequently used in automation and metering, distribution control and monitoring, and distribution protection. However, a significant challenge arises from the uncertainty surrounding the genuine identity of data recipients. In this paper, we propose an efficient and novel covert channel that leverages the IEEE 802.11 DCF to transmit data requiring a high level of security. It is also the world’s first covert channel supporting quality of service (QoS). Our protocol was implemented and tested in the ns-3 simulator, achieving very high-performance results. Its performance remains robust even under saturated network conditions with additional background traffic generated by other stations. This covert channel presents a novel approach to securely transmitting large amounts of QoS data within the smart grid.

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers.

An SVR-based and Location-aware Method for Mobile QoS Prediction

With the rapid development of intelligent mobile communication technology, the num­ber of mobile services and the number of mobile users are both continuously increasing. So, the services used by a user can only account for a very small proportion of the existing services, which results in a sparse user­service quality of service (QoS) matrix. However, QoS is critical for service selection and service recommendation. Therefore, predicting the unknown values of the sparse QoS matrix is essential. However, due to the sparsity of QoS data, the QoS predic­tion accuracy is difficult to improve. Faced with the problem, this paper intends to utilize the outstanding generalization ability and only support vectors dependent property of support vector regression (SVR) to overcome the difficulty brought by the sparsity of data and predict the un­known QoS more accurately. Moreover, it is evident that in the mobile environment, QoS values are closely related to the locations of the invoking users. Therefore, this paper intends to improve the accuracy of QoS prediction by incorporating not only the information of similar users but also the information of nearby users into feature vectors. On the other hand, the known QoS values of nearby users can be used to roughly estimate the unknown QoS values of the cold­start user, so as to alleviate the cold­start problem to some extent. Thus, a location­aware SVR­based method for QoS prediction (SVR4QP) is proposed. Compared with some classical QoS prediction algorithms, the experimental results show that in 1/3 of the cases, SVR4QP is moderate; in 1/6 of the cases, SVR4QP is suboptimal; and in half of the cases, SVR4QP is optimal. Compared with some novel mobile QoS prediction methods, the experimental results show that in 1/4 of the cases, SVR4QP is moderate; in half of the cases, SVR4QP is suboptimal; and in 1/4 of the cases, SVR4QP is op­timal. All these indicate that SVR4QP has comparatively more accurate mobile QoS prediction.

Temporal pattern-aware QoS prediction by Biased Non-negative Tucker Factorization of tensors

Dynamic quality of service (QoS) data contain rich temporal patterns of user-service interactions, which are vital for better understanding user behaviors and service conditions. Canonical polyadic (CP)-based latent factorization model has proven to be capable of capturing such patterns. However, it models the relations among latent features of user, service and time in a rigid and unnatural way, causing its failures in capturing the complex patterns when the target QoS data become massive. To address that issue, this paper proposes a Biased Non-negative Tucker Factorization of 3D tensors (BNTucF) model with the four-fold ideas: (1) utilizing a core tensor for modeling the complex interactions among latent features; (2) incorporating linear biases into the model for accurate descriptions on QoS fluctuation; (3) constraining the model to be non-negative for describing QoS non-negativity; (4) deducing a single latent factor-dependent, multiplicative updating scheme for training the model in an efficient density-oriented way. Empirical studies demonstrate that the proposed BNTucF can learn complex dynamic user-service interaction patterns more accurately, hence achieving accurate predictions on missing QoS data.

Optimization of product design process in cloud manufacturing system based on swarm intelligence

ABSTRACT Currently, manufacturing industries in many countries are grappling with an imbalance in the supply and demand of manufacturing resources, encompassing resource scarcity and idleness, as well as insufficient and surplus manufacturing capabilities. To address this issue, cloud manufacturing has emerged, integrating the Internet of Things, cloud computing, and advanced manufacturing technologies. This fusion enables the on-demand allocation of manufacturing resources and capabilities, facilitating efficient networked production. However, in the realm of the Internet, the challenge lies in efficiently aligning abundant information with a company’s manufacturing objectives to genuinely deliver an “on-demand service.” This represents a pivotal issue that demands immediate resolution within the operational framework of cloud manufacturing platforms. To effectively address the optimization challenge in Manufacturing Cloud Services (MCS), this study leverages two dimensions: the fundamental Quality of Service (QoS) of MCSs and the interrelated QoS among these services. A holistic QoS model for assembling MCSs is constructed. In this model, the timely relevance of fundamental QoS data is ensured through the incorporation of a temporal weighting function, while an enhancement in the precision of optimization is achieved by considering the interconnections between service combinations. The final simulation experiments showcase the effectiveness of the proposed approach in this paper.

Biased collective latent factorization of tensors with transfer learning for dynamic QoS data predicting

The latent factorization of tensors (LFT) develops an efficient way to fulfill the prediction for dynamic quality of service (QoS) data and it has gained much attention in the research field of recommend systems. However, the LFT might perform poorly or even be invalid when data missing and fluctuation occur. To address this problem, a novel LFT called biased collective LFT with transfer learning (BCLFT_TL) is put forward. The main ideas include 1) establishing an auxiliary tensor related to the target tensor and transferring the knowledge from the auxiliary domain to the target domain to mitigate the data sparsity, 2) incorporating linear biases on each dimension of tensors into the objective function to cope with data fluctuations, and 3) a set of learning rates is set delicately to guarantee the non-negativity of the target data and the mini-batch additive gradient descent method is used to reduce the computational burden. Finally, the superiority of the BCLFT_TL-based model is confirmed on six real datasets from two data sources compared with state-of-the-art methods.

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.

Poisoning QoS-aware cloud API recommender system with generative adversarial network attack

With the proliferation and deepening of service-oriented architecture, more and more enterprises and organizations are exposing their computing functions and big data to the Internet in the form of cloud APIs to support service-oriented software development. This has resulted in a plethora of cloud APIs with similar functionality appearing on the Web, drowning users in a sea of cloud API choices. To solve this problem, quality of service (QoS)-aware recommender system is then widely applied to the selection of cloud APIs. Due to the dynamic and open network environment, the QoS-aware cloud API recommender systems are vulnerable to data poisoning attacks, where attackers inject poisoned data to skew the recommender system and make the recommendation direction follow the attacker's will. Given the lack of data poisoning attack methods and robustness analysis for existing QoS-aware cloud API recommender systems, in this work, we first built a general poisoning attack framework for QoS-aware cloud API recommender systems to elucidate and standardize the attack process. Then, we proposed a deep learning-based poison attack approach, which uses generative adversarial network (GAN) to learn the cloud API QoS data distribution of real users in an adversarial way, so as to generate high-quality fake user attack vectors. We conducted extensive experiments on real-world QoS datasets, and the experimental results show that our proposed GAN-based poisoning attack is effective and can better hide itself from being detected. In addition, we analyzed the data poisoning attack mechanism and the robustness of the cloud API recommender system based on four categories of twelve recommendation methods, thereby raising awareness about the security of cloud API recommendation and helping the recommender system defenders to develop more targeted defense strategies.

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.

Usefulness of QoS in Multidimensional QoE Prediction for Haptic–Audiovisual Communications

This article investigates prediction of QoE (Quality of Experience) by comparing borrowing–from–neighbor situations and isolated ones. We demonstrate that joint utilization of multiple QoE measures enhances the accuracy of QoE prediction compared to that by a collection of individual QoE measures each regressed on QoS (Quality of Service) parameters, while the accuracy improvement with additional usage of QoS information in the former is limited. As an example of system which needs multidimensional QoE representation, the article gives haptic audiovisual interactive communications. We employ QoE and QoS data taken previously in an experiment, where 13 QoE measures (a five–point score each) and 12 QoS parameters (nonnegative continuous values each) are available at three average rates of load traffic. We build two kinds of Bayesian models for QoE prediction; one is a logistic regression model of a single QoE measure as the response variable and QoS parameters as predictors, which is a typical traditional method of discrete QoE prediction and isolated in a sense. The other is a structural equation model (SEM) with latent constructs (i.e., factors) of audiovisual quality, haptic quality and user experience quality; the original SEM, which contains only QoE indicators of three constructs (AVQ, HQ and UXQ), was proposed in one of the author’s previous studies. This article extends the SEM in order to accommodate QoS parameters. We develop two kinds of new SEMs with QoS parameters: One has three extended constructs referred to as eAVQ, eHQ and eUXQ, each of which has both QoE and QoS indicators, and the other has separate constructs for QoE and QoS, which lead to totally six constructs (AVqoe, Hqoe, UXqoe, AVqos, Hqos and UXqos). We performed Markov chain Monte Carlo (MCMC) simulation of the Bayesian models with the JAGS software in an R environment. For comparison of QoE prediction accuracy, we adopt the 10–fold cross validation method and in part WAIC (widely applicable information criterion). We then found that the three–construct models outperform the logistic regression models with respect to all subjective QoE measures and that the two kinds of the models are comparable as for the objective measure. The six–construct model exhibits almost the same accuracy as that of the three-construct one unless the number of QoE measures ( n qoe ) in the model is small. When the number n qoe is small, single–construct models may be a better choice. We have thus learned that multiple QoE measures should be utilized jointly (i.e., borrowing from neighbor) in QoE prediction rather than resorting to QoS information only.

Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis

Historical Quality-of-Service (QoS) data describing existing user-service invocations are vital to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) models based on Matrix Factorization (MF) have proven to be highly efficient in performing representation learning in QoS data. However, its performance is hindered by its linear inherence and implicit encoding of collaborative QoS signal. To address this critical issue, we present a novel approach in this paper, dubbed as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>T</u></b> wo-stream <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>G</u></b> raph convolutional network-incorporated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>L</u></b> atent <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>F</u></b> eature <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>A</u></b> nalysis (TGLFA). The proposed TGLFA is significantly different from previous approaches to representation learning in QoS data in the following three aspects. First, it constructs a multilayer fully-connected network to capture the attribute characteristics representing the nonlinear latent features of service. Second, TGLFA constructs a biparty graph to represent the user-service interactions, where the light graph convolutional network is adopted to acquire the high-order connectivity in QoS data. Last, Aiming to improve computational efficiency, the proposed approach considers the mechanism for data density-oriented modeling when building the input and output layers. Detailed experimental results on eight large-scale cases constructed on two real QoS datasets demonstrate that the proposed TGLFA significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency. The notable results show that TGLFA is a novel and effective approach to QoS data representation learning.

Spatial Context-Aware Time-Series Forecasting for QoS Prediction

With the explosive growth of web services, the increasing sparse and dynamic QoS (quality of service) data pose a great challenge to QoS prediction in service recommendation. How to fully utilize the contextual information of service invocation and their potential relationships becomes the key to improving the accuracy of QoS prediction. In this paper, a spatial context-aware time series forecasting (SCATSF) framework is proposed for QoS prediction by considering both the temporal and the spatial context of users and services. SCATSF has three main parts: time-aware neighbors selection, spatial context-aware interaction learning and time series forecasting for QoS prediction. First, a novel time series similarity (TSS) is proposed to measure the similarity of users or services based on time-varying QoS fluctuation, in order to select appropriate neighbors for the target user or service. Furthermore, the contextual information of neighbors, as well as the contextual information of users and services, are both integrated to enrich the spatial context of service invocation, and a pairwise multi-layer deep network (PMLDN) is developed to consolidate multiple features and to learn feature interactions in pairs. With the help of spatial contextual information and time-aware QoS information, a spatial context-aware GRU model (SCA-GRU) is finally present to complete the time series forecasting for QoS prediction. Experimental results on the prediction of response time in a real-world dataset demonstrate that our SCATSF approach can effectively utilize spatio-temporal contextual information, so that it can achieve higher accuracy than many existing methods.

MultiFed: A fast converging federated learning framework for services QoS prediction via cloud–edge collaboration mechanism

Federated learning (FL) has become a common approach for distributed training of quality of service (QoS) prediction tasks in smart city solutions. However, FL is vulnerable to heterogeneity. Existing FL-based QoS prediction methods usually update the global model by directly aggregating the divergent local gradients computed on heterogeneous datasets, which does not effectively capture the heterogeneity of users’ QoS data distribution and requires many rounds of training and much communication time to achieve satisfactory accuracy. In addition, the high communication latency between the central server and the client further slows down the convergence process. To address the above issues, we propose a multi-centre federated learning framework for services QoS prediction via a cloud–edge collaboration mechanism, namely MultiFed. In this framework, we sink the central server in traditional FL from cloud to edge and train a global model in each edge region. The edge server performs two gradient aggregation strategies to update the global model in each training round. One is an internal aggregation strategy for regional users; the other is an external aggregation among edge servers with the assistance of a cloud server. Extensive experiments have shown that 9.52%∼40.00% reduces the communication delay, and 1.91%∼38.89% reduces the communication rounds required to reach the target prediction in this method compared to existing methods, validating the effectiveness of MultiFed.

A Personalized Federated Tensor Factorization Framework for Distributed IoT Services QoS Prediction From Heterogeneous Data

With a growing number of alternative Internet of Things (IoT) services that provide the same functionalities, Quality-of-Service (QoS) prediction has become an important research issue. Conventional central methods require that the historical QoS data is centralized. While the QoS data may be distributed in different edge servers. Due to privacy, these edge servers may not be willing to share their data with others for a better model representation. Besides, as each edge server is in charge of the collecting QoS data from the users in a specific area, the QoS data are likely to be heterogeneous. In this article, we propose a personalized federated tensor factorization framework for distributed privacy-preserving IoT services QoS prediction. We first adopt tensors to represent the QoS data with multidimensions and initialize a personalized model for each edge server. Then, each edge server performs local tensor factorization and exchanges the public component with a parameter master. By constraining the consistency of the global public component and the local personalized public components, the global model can learn information from edge servers during the training process. In addition, we conduct extensive experiments on a real-world QoS data set, the experimental results demonstrate that the proposed framework is efficient and effective.

QoS-Aware Downlink Traffic Scheduling for Cellular Networks with Dual Connectivity

In a cellular network, how to preserve users’ quality of service (QoS) demands is an important issue. To provide better data services, researchers and industry have discussed the deployment of small cells in cellular networks to support dual connectivity enhancement for user equipments (UEs). By such an enhancement, a base station can dispatch downlink data to its surrounding small cells, and UEs that are located in the overlapping areas of the base station and small cells can receive downlink data from both sides simultaneously. We observe that previous works do not jointly consider QoS requirements and system capabilities when making scheduling decisions. Therefore, in this work, we design a QoS traffic scheduling scheme for dual connectivity networks. The designed scheme contains two parts. First, we propose a data dispatching decision scheme for the base station to decide how much data should be dispatched to small cells. When making a dispatching decision, the proposed scheme aims to maximize throughput and ensure that data flows can be processed in time. Second, we design a radio resource scheduling method, which aims to reduce dropping ratios of high-priority QoS data flows, while avoiding wasting radio resources. In this work, we verify our design using simulation programs. The experimental results show that compared to the existing methods, the proposed scheme can effectively increase system throughput and decrease packet drop ratios.

A QoS Prediction Approach Based on Truncated Nuclear Norm Low-Rank Tensor Completion.

With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services. In the real MEC service invocation environment, due to time and network instability factors, users’ QoS data feedback results are limited. Therefore, effectively predicting the Qos value to provide users with high-quality services has become a key issue. In this paper, we propose a truncated nuclear norm Low-rank Tensor Completion method for the QoS data prediction. This method represents complex multivariate QoS data by constructing tensors. Furthermore, the truncated nuclear norm is introduced in the QoS data tensor completion in order to mine the correlation between QoS data and improve the prediction accuracy. At the same time, the general rate parameter is introduced to control the truncation degree of tensor mode. Finally, the prediction approximate tensor is obtained by the Alternating Direction Multiplier Method iterative optimization algorithm. Numerical experiments are conducted based on the public QoS dataset WS-Dream. The results indicate that our QoS prediction method has better prediction accuracy than other methods under different missing density QoS data.

Assessing cloud QoS predictions using OWA in neural network methods

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

Privacy-Preserving QoS Forecasting in Mobile Edge Environments

Mobile Edge Computing is an emerging technology offering low latency responses by deploying edge servers near mobile devices. We propose a novel privacy-preserving QoS forecasting approach – Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments) to address the challenges of user mobility and information leakage encountered by QoS forecasting in mobile edge environments. Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users’ accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

Possibilities for Assessment and Geovisualization of Spatial and Temporal Water Quality Data Using a WebGIS Application

The provision of webGIS-based water quality data services has become a priority area for both the public and administrative sectors in the context of the pandemic emergency associated with the global spread of COVID-19. Current geographic, monitoring and decision supporting systems, typically based on web-based geospatial information, greatly facilitate the sharing of spatial and temporal data from environmental databases and real-time analyses. In the present study, different water quality indices are determined, compared and geovisualized, during which the changes in the quality of the shallow groundwater resources of a settlement are examined in the period (2011–2019) in an eastern Hungarian settlement. Another objective of the research is to determine three water quality indices (Water Quality Index, CCME Water Quality Index, Contamination degree) and categorize water samples based on the same input spatial and temporal data using self-developed freely available geovisualization tools. Groundwater quality was assessed by using different water quality indices. Significant pollution of the groundwater in the time period before the installation of a sewage network was shown. Regarding water quality, significant positive changes were shown based on all three water quality indices in the years after installing a sewage network (2015–2019). The presence of pollution apart from the positive changes suggests that the purification processes will last for a long time.