Quality Of Service Prediction Research Articles

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

QoS prediction of cloud services by selective ensemble learning on prefilling‐based matrix factorizations

SummaryWhen selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability.

DiSA-CF: A distance-integrated self-attention model for collaborative filtering in web service recommendation

The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user’s specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments.

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.

Estimation of Road Service Quality Using the Two‐Fluid Model Considering the Resilience of Traffic Flow

In an urban road network, the ability of the traffic flow itself to alleviate congestion caused by external disruptions is overlooked. This study applied the two‐fluid model to simulate mesoscopic traffic flow, focusing on the resilience of urban road networks under normal disturbances. Three resilience indices—plasticity, transition of elasticity, and elasticity—were introduced based on the failure deformation process of rigid materials. These indices were used to modify the two‐fluid model’s parameters, considering the effects of bus operations and temporary roadblocks on traffic flow and service quality. A hidden Markov model (HMM) was employed to predict service quality transitions (distinction, merit, and pass), with validation using dynamic bayonet traffic data from Xuancheng and video recordings from Xi’an, China. The results confirmed that resilience varies significantly across different times and locations, with peak hours and dense urban areas exhibiting lower resilience and higher susceptibility to disruptions. Bus queuing was found to degrade service quality, and rainstorms had a more severe impact than construction zones. The study can aid in the development of management efficiency of urban road networks.

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.

Vehicle-to-Everything: Cybersecurity and Quality of Service Challenges

This paper is focused to the emerging technological development in the area of the advanced vehicular communication systems. It considers the most up to date Vehicular to Everything technologies and discusses the typical scenarios related to the communication between vehicles, between vehicles and infrastructure, vehicles and pedestrians and communication between vehicles and network environment. Paper also considers the arising issues and countermeasures related to the security, privacy and quality of service (QoS) as well as predictive QoS aspects.

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%.