Quality Of Service Prediction Research Articles

QoS prediction using EMD-BiLSTM for II-IoT-secure communication systems

Abstract To address the challenges of secure and reliable communication and system quality of service (QoS) prediction in intelligent production lines (IPL) in the Industrial Intelligent Internet of Things (II-IOT) environment, a redundant collaborative security model-based communication architecture is designed. First, the redundant collaborative security communication model is introduced to construct the network communication architecture of IPL, including the industrial-site mechanical floor, data awareness layer, and gateway and application layer. Then, to leverage the advantages of the empirical-mode decomposition (EMD) method and the bidirectional long short-term memory (BiLSTM) model in time-series data analysis and processing, an EMD-BiLSTM-based QoS prediction model is proposed that can synchronously achieve one-step and multi-step prediction of QoS attributes. The proposed model exhibits a prediction accuracy of up to 94.01% on the SourceForge dataset, with prediction, recall, and F1 values as high as 91.37, 90.60, and 90.99%, respectively. The proposed EMD-BiLSTM model can achieve better performance than the state-of-the-art QoS prediction models, indicating that the proposed model can be more effectively used to improve the reliable communication level of II-IoT.

Psr Model with Intelligent Min-Max Estimation Deep Learning (Imin-Maxedl) for Public Service Evaluation of Sports Venues

Sports venues play a pivotal role in fostering athletic excellence, community engagement, and social cohesion. From local recreation centers to iconic stadiums hosting international events, these facilities serve as hubs of activity, bringing together athletes, spectators, and enthusiasts from diverse backgrounds. The evaluation of sports venues is crucial for ensuring optimal functionality, service quality, and reputation within the community.
 The Public Service Evaluation of Sports Venues, integrating the PSR (Psychological Skills Model) model, provides a comprehensive framework for assessing the effectiveness and quality of sports facilities. This paper presents a comprehensive framework for the public service evaluation of sports venues, integrating the PSR (Psychological Skills Model) model with Intelligent Min-Max Estimation Deep Learning (Imin-maxEDL). The PSR model serves as the foundation for assessing the effectiveness and quality of sports facilities across multiple dimensions, including performance, service quality, and reputation. Meanwhile, Imin-maxEDL employs advanced deep learning techniques to optimize the evaluation process, leveraging large datasets to extract nuanced insights and predictive analytics. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, demonstrating significant improvements in accuracy, efficiency, and predictive capabilities compared to traditional evaluation methods. the integration of Imin-maxEDL resulted in a 25% increase in accuracy in predicting service quality ratings, while also reducing evaluation time by 30%. Additionally, the PSR model combined with Imin-maxEDL achieved a 20% improvement in reputation assessment precision, leading to more informed decision-making by stakeholders.

A Novel Deep-Learning-Based QoS Prediction Model for Service Recommendation Utilizing Multi-Stage Multi-Scale Feature Fusion With Individual Evaluations

With the rapid development of service computing, the demand for service recommendation is increasing. Quality of Service (QoS) prediction has been one of the key challenges for service recommendation. Existing deep learning-based methods have been proposed for QoS prediction, but further improvement of their neural network structures is still needed to improve the prediction accuracy. This work introduces multi-stage multi-scale feature fusion with individual evaluations to a deep learning model for accurate QoS prediction. In our model, non-negative matrix factorization is used to extract three-scale (i.e., global, local, and individual) features; distance similarity is exploited to find similar users and services; a multi-stage deep neural network is designed to fuse multi-scale features, where individual evaluations are input to each stage to correct QoS features. Finally, our model is compared with often-cited prediction methods, and the experimental results show that it can more accurately predict QoS than its peers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Accurate QoS prediction is very helpful to recommend the most suitable services to users among many similar services. Affected by the sparsity of historical data, the accuracy of existing QoS prediction methods is often limited. The multi-scale features of users and services can be used to improve prediction accuracy. This work proposes a new QoS prediction method to do so. Specifically, it first extracts global and individual features through non-negative matrix factorization and uses distance similarity to obtain local features. Then, it proposes a new deep neural network that fuses the extracted multi-scale features in each learning stage, thereby improving QoS prediction for services recommendation.

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

With the rapid development of intelligent mobile communication technology, the num&amp;shy;ber 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 user&amp;shy;service 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 predic&amp;shy;tion 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 un&amp;shy;known 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 cold&amp;shy;start user, so as to alleviate the cold&amp;shy;start problem to some extent. Thus, a location&amp;shy;aware SVR&amp;shy;based 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&amp;#894; in 1/6 of the cases, SVR4QP is suboptimal&amp;#894; 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&amp;#894; in half of the cases, SVR4QP is suboptimal&amp;#894; and in 1/4 of the cases, SVR4QP is op&amp;shy;timal. 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.

Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning

Airport service quality ratings found on social media such as Airline Quality and Google Maps offer invaluable insights for airport management to improve their quality of services. However, there is currently a lack of research analysing these reviews by airport services using sentimental analysis approaches. This research applies multiclass models based on Aspect-Based Sentimental Analysis to conduct a comprehensive analysis of travellers’ reviews, in which the major airport services are tagged by positive, negative, and non-existent sentiments. Seven airport services commonly utilised in previous studies are also introduced. Subsequently, various Deep Learning architectures and Machine Learning classification algorithms are developed, tested, and compared using data collected from Twitter, Google Maps, and Airline Quality, encompassing travellers’ feedback on airport service quality. The results show that the traditional Machine Learning algorithms such as the Random Forest algorithm outperform Deep Learning models in the multiclass prediction of airport service quality using travellers’ feedback. The findings of this study offer concrete justifications for utilising multiclass Machine Learning models to understand the travellers’ sentiments and therefore identify airport services required for improvement.

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering

Nowadays, there is a large number of web services with similar functions, from which users choose the best according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship. However, the low-dimensional linear and the high-dimensional nonlinear relationships between users and services are seldom considered simultaneously. In addition, although location information, including the local location information of users and services, is incorporated to overcome data sparsity in most approaches. The global location information is seldom considered. Aiming at the above shortcomings, we propose a new QoS prediction model that fuses local and global location information of users and services in the interaction layer of the model. The proposed model uses a multilayer perceptron (MLP) to acquire high-dimensional nonlinear relationships of users and services, where the dot product is employed in complementing the learning of low-dimensional linear relationships. Experimental results on the real world dataset WS-Dream validate the prediction performance of the proposed model.

Work Environment, Human Resource Practices, and Organizational Culture: A Causal Model on Service Quality of Employees in Government Agency in Davao Region

Purpose: This quantitative, non-experimental study aimed to establish the best-fit structural model for the service quality of employees in a national government agency, with a specific focus on the Department of Environment and Natural Resources (DENR) in the Davao Region. Its significance lies in contributing to the existing body of knowledge in the field of public administration, leading to a more efficient and effective delivery of public services. Examining the relationship between factors such as the work environment, human resource practices, and organizational culture concerning service quality, this study builds upon the conclusions drawn from international research, which has investigated these factors in other countries but has yet to address the specific context of the Philippines, particularly in the Davao Region. Therefore, this study would address the gap by examining these relationships in the local context, contributing valuable insights to the literature on service quality in the Philippines.&#x0D; Methodology: A standardized instrument, administered through the Google platform, was used to survey 400 rank-and-file employees selected via stratified sampling in the Davao Region. The data collected were analyzed using statistical methods, including mean and standard deviation calculations, Pearson correlation analysis, multiple regression, and structural equation modeling (SEM).&#x0D; Findings: The study revealed that the service quality (dependent variable) was very high, while the work environment, human resource practices, and organizational culture (independent variables) were all at high levels. A Pearson correlation analysis unveiled a statistically significant and positively correlated relationship between the independent and dependent variables. Multiple regression analysis indicated that work environment, human resource practices, and organizational culture influence service quality. Moreover, organizational culture was found to be the best influencer of service quality of employees. Significantly, Model 5 emerges as the most fitting structural model for assessing service quality. The model demonstrated the direct influence of the work environment on service quality. Additionally, the SEM model identified specific variables that can predict service quality: training and development and evaluation and supervision (for human resource practices), physical and social work environment (for work environment), and adaptability, consistency, and involvement (for organizational culture).&#x0D; Recommendations: Therefore, the discoveries from this study offer valuable insights with direct relevance to policymakers and managers seeking to improve service quality in national government agencies.

Accurately Predicting Quality of Services in IoT via Using Self-Attention Representation and Deep Factorization Machines

The past decade has witnessed a widespread adoption of IoT devices and services in various applications such as intelligent transportation systems. It is crucial for IoT applications to select high-quality services to boost their reliability and efficiency. The prediction for Quality of Service (QoS) can be used to address this issue. Although a number of QoS prediction approaches have been proposed, their performance may be limited in the IoT environment where context features can significantly impact QoS predictions. Based on the historical QoS records of services, this paper proposes a collaborative QoS prediction approach using self-attention representation and deep factorization machine. The approach first leverages the global and local contextual information of services and users to learn personalized representations. Then, based on the personalized representations, it utilizes a deep factorization machine to make QoS predictions. Extensive experiments conducted on a real-world dataset show that the proposed QoS prediction approach significantly outperforms the other state-of-the-art approaches in terms of prediction accuracy.

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.

New data about library service quality and convolution prediction

Library service quality, one of the key performance indicators of service qualities in universities, has been considered deeply in management strategies as part of the Fourth Industrial Revolution, especially, after the Covid-19 pandemic. We undertook a survey around Universities in Ho Chi Minh City and Tien Giang University, Vietnam focused on freshmen and sophomores to assess library service quality for improving the learning service quality. Machine learning has been deployed for predicting the library service, quality, and has been adopted successfully in depicting the assessment results. To perform the effectiveness of data, the Convolution Bidirectional Long-Short Term Memory (Conv-BiLSTM), and Convolution Bidirectional Gated Recurrent Unit (ConvBiGRU) were used. The models have illustrated appropriate performances when providing sufficient accuracy and extracting the prediction of the output.

Service Recommendation Model Based on Trust and QoS for Social Internet of Things

The Social Internet of Things (SIoT) is a novel network that integrates social relations among objects and facilitates the interconnection between humans and smart devices through the Internet of Things (IoT). However, with the growing number of users and their devices in SIoT, ensuring high quality of service (QoS) and trust relations among users poses significant challenges. Therefore, this paper proposes a SIoT services recommendation model based on trust and QoS. Our model combines user trust relations and QoS prediction, including the availability, reliability, and efficiency of services. To achieve this, we propose a three-layer services recommendation model consisting of a social network layer, a devices layer, and a services layer. We suggest using direct trust and joint trust to accurately calculate users' trust relations. Additionally, we introduce an Levenberg-Marquardt (L-M) -based algorithm for social network users' trust community clustering and a Random Service System (RSS) -based algorithm for QoS prediction. Experimental results show that our proposed model effectively recommends services in SIoT that exhibit high trustworthiness and quality.

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

RAPID: Enabling fast online policy learning in dynamic public cloud environments

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with strict Quality of Service (QoS) requirements. Although recent approaches have demonstrated promising results, those works remain largely impractical in public cloud environments since workloads are not known in advance and may only run for a brief period, thus prohibiting offline learning and significantly hindering online learning. In this paper, we propose RAPID, a novel framework for fast, fully-online resource allocation policy learning in highly dynamic operating environments. RAPID leverages lightweight QoS predictions, enabled by domain-knowledge-inspired techniques for sample efficiency and bias reduction, to decouple control from conventional feedback sources and guide policy learning at a rate orders of magnitude faster than prior work. Evaluation on a real-world server platform with representative cloud workloads confirms that RAPID can learn stable resource allocation policies in minutes, as compared with hours in prior state-of-the-art, while improving QoS by 9.0x and increasing best-effort workload performance by 19%–43%.

Informer-based QoS prediction for V2X communication: A method with verification using reality field test data

Vehicle-to-everything (V2X) communication plays a critical role in connected and automated driving applications, which requires strict Quality of Service (QoS) performance in terms of delay and reliability since human safety is involved. The QoS of V2X communications is impacted by various factors, such as radio interference, mobility, and user equipment (UE) density, making accurate prediction challenging. Various methods have been exploited to predict QoS deterioration. However, they usually require high computational complexity or may not capture the complex features of V2X communication environment. In this paper, a novel Informer-based QoS prediction model with a causal convolution self-attention mechanism is presented, which achieves high prediction performance with minimal computing resources. Notably, the test data used in the evaluation of the model is collected from the National Intelligent Vehicle and Intelligent Transportation Demonstration Zone in Daxing District, Beijing. The experimental results demonstrate that the proposed method exhibits superior accuracy and stability compared to existing methods such as Back Propagation (BP), ELMAN, LSTM, and CNN.

Measuring service quality based on customer emotion: An explainable AI approach

This paper develops an explainable artificial intelligence (AI) approach to measuring service quality in voice-based service encounters. Drawing from the psychology and computer science literature, we construct features of a customer's emotion dynamics during a service encounter. Using real-world call center data from a large insurance company, we train an ensemble model with these emotion dynamics features to predict service quality. The model has higher prediction performance than the two benchmark approaches using quality-assurance evaluation and operational indices. Our method for emotion dynamics classification outperforms a host of state-of-the-art time series classification algorithms. We further apply explainable AI methods to identify the most important features of emotion dynamics and show how they are related to service quality. For example, the location where the last emotion episode appears in a service call has a U-shaped relationship to low quality. Finally, to demonstrate utility, we design an IT artifact to automatically measure service quality after service encounters in the call center and use the measure to predict a customer's referral intention.

Gated Multi-task Learning for QoS-aware Service Recommendation

In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very interested in. A key challenge is how to get personalized and accurate QoS values of web services from the multi-source information of users/services. In addition, complex contexts and multi-tasking attributes also make the collaborative services quality prediction difficult to meet. Inspired by the progress of multi-task deep learning, this paper proposes a new framework of service quality prediction called multi-task gated expert network (MGEN), which is based on gating mechanism and multi-task learning. The MGEN consists of three parts: expert-based context-source feature extraction, gated interaction learning and information fusion. First, multiple expert networks are used to capture the latent representation in raw data of web services, including both implicit features in the QoS matrix and explicit features of service descriptions. In the process, several independent experts extract features from different dimensions. Second, output embedding of different expert networks is transmitted to each task layer through different weights, learning both global and local high-order feature interactions. Third, the interactive information of different tasks is fused by embedding splicing, and then fed back to the prediction module for final service recommendation. Results of the comparative experiment demonstrate that the proposed MGEN significantly outperforms the state-of-the-art models of service recommendation, where both the implicit and explicit information of various multi-source features and interaction sequences are effectively extracted. In addition, the gating mechanism performs well in the QoS prediction by independently modeling each task.

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.