Quality Of Service For Applications Research Articles

Design and implementation of an integrated OWC and RF network slicing-based architecture over hybrid LiFi and 5G networks

AbstractRadio frequency (RF) systems tend to become congested and overused due to the increasing number of users, devices and the multiple technologies involved in their deployment. This leads to the downgrading of quality of service (QoS) further caused by interference with different signals. Optical Wireless communications (OWC) are emerging as a feasible alternative as they offer unlicensed, interference-free spectrum by using the frequency range located in the visible and invisible light spectrum. Its applications can be found in various fields such as healthcare, education, finance and industry 4.0. Moreover, it enhances the security and privacy of communications. Nevertheless, the limited spectrum in OWC also requires optimised resource allocation to support the QoS of different applications or users whilst lacking established infrastructure to manage this. To address these challenges, this paper proposes a novel 5G-LiFi framework able to ensure QoS requirements by introducing network slicing in Light Fidelity (LiFi) networks integrated with 5G infrastructure. This paper has developed and deployed a 5G-LiFi architecture capable of providing network slicing capabilities over the LiFi segment of the hybrid network. It allows a full control over the network traffic and tailored, improved QoS capabilities. The proposed solution has been empirically validated and evaluated in a realistic testbed employing real-world LiFi and 5G network equipment, and yielded promising results in terms of bandwidth, delay, jitter and packet loss. This work concludes that the use of heterogeneous networks integrating OWC with RF is a suitable solution and it can lead to a better use and exploitation of the different spectrums, improving the QoS offered to end-users.

The Ease of Use of Applications and Quality of Service Toward Consumer Loyalty in Cargo Express Service Companies, Bekasi, Indonesia

This study tries to assess the effect that may occur among the ease of use of applications on consumer loyalty, quality of service on consumer loyalty, ease of use of applications, and quality of service on consumer loyalty. This study aims to determine how important application usability and service quality are influencing consumer loyalty.This study, discusses the factors that can affect consumer loyalty and determines how much influence the ease of use of applications and service quality have on consumer loyalty. This research utilized purposive sampling. The data gathering approach utilized in this study is questionnaires with Google Forms media. The prerequisite test from the data obtained uses the data quality test and the classical assumption test. This study employed the following data analysis techniques: validity test, reliability test, normality test, multicollinearity test, heteroscedasticity test, multiple regression analysis, statistical t test (partial), F test (simultaneous), and coefficient of determination (R2). The final results show that the two independent variables (the ease of use of applications, quality service) were significantly affected by the dependent variable (consumer loyalty). According to the report, service quality and application simplicity of use account for 42.6% of client loyalty. Keywords: the ease of use of applications, service quality, consumer loyalty

Assessing the psychological pathways of esports events spectators: an application of service quality and its antecedents and consequences

ABSTRACT Research question This research develops and tests a comprehensive structural model to explain the psychological pathway behind esports spectator behaviours, linking perceived service quality to its antecedents (i.e. extrinsic motivation and intrinsic motivation) and consequences (i.e. satisfaction and revisit intention). Research methods Two rounds of data collection were completed for Phase One (N = 485) and Phase Two (N = 217). The measurement items were borrowed from a range of pre-existing scales of traditional sport or esports. Phase One was designed to assess the psychometric properties of the measurement model. Phase Two was purported to test the pathways among the constructs as hypothesised in the structural model. Results and findings In Phase One, the model fit indices showed good model fit, reliability and construct validity. In Phase Two, the SEM model fitness indices showed a marginally acceptable model fit. The path analysis supported 15 out of 19 hypotheses; no significant relationships were found between relatedness and intrinsic motivation, autonomy and extrinsic motivation, intrinsic motivation and physical environment, and intrinsic motivation and interaction. Overall, the structural model showed a moderate to substantial predictive power. Implications The study provides empirical evidence for the proposed relationships among the constructs and expands the realm of the current theories to the context of esports, especially the application of the self-determination theory and the new esports service quality model. It also provides a more comprehensive picture for esports practitioners to understand their spectators and balance their effort in managing elements which promote future attendance.

Analyzing User Experience and User Satisfaction: Evaluating User Acceptance of the Halo Hermina App

This research investigates the factors influencing user acceptance of the Halo Hermina mobile health application through an analysis of user experience and satisfaction. The study utilized a survey method to gather feedback from Halo Hermina users, assessing the questionnaire's validity and reliability. The results indicate strong validity across most items, with correlation values between 0.779 and 0.828 for performance expectancy and over 0.77 for effort expectancy. The reliability analysis shows high internal consistency, with Cronbach's Alpha values exceeding 0.976. User satisfaction scored the highest mean (4.027), indicating a consistent high level of satisfaction among users. The correlation analysis reveals significant relationships between performance expectancy, effort expectancy, facilitating condition, and behavioral intention, with the strongest correlation found between performance expectancy and effort expectancy (0.8796). Overall, the study emphasizes the crucial role of enhancing user experience and satisfaction to boost the adoption of mobile health applications like Halo Hermina, providing valuable insights for developers and stakeholders to enhance application features and service quality to meet user expectations effectively.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

The Influence of Mobile Application User Experience, Service Quality, and Social Interaction on Customer Satisfaction Quantitative Research in the Online Service Industry

The Influence of Mobile Application User Experience, Service Quality, and Social Interaction on Customer Satisfaction Quantitative Research in the Online Service Industry

Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.

HCSMBO: A hybrid cat swarm and monarch butterfly optimization algorithm for energy consumption optimization in industrial internet of things

Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant waste of computing resources and low efficiency. This paper proposes a Hybrid Cat Swarm Optimization and Monarch Butterfly Optimization (HCSMBO), which combines the advantages of Cat Swarm Optimization (CSO) and Monarch Butterfly Optimization (MBO) to solve this problem. By utilizing the characteristics of swarm intelligence, including strong spatial search ability, high stability, and fast convergence, HCSMBO focuses on detailed optimization of the coverage, efficiency, and energy balance coefficient of Support Vector Machine (SVM) networks. By adjusting the weight factors of the fitness function, this algorithm can effectively find the global optimal solution. The experimental results confirm that HCSMBO significantly improves the intelligence, accuracy, and stability of task scheduling in resource management configuration, while achieving lower energy consumption. The research results of this paper are of great significance for improving the service quality of IIoT applications, meeting personalized business needs, and improving resource utilization and execution efficiency. In addition, they also provide new ideas and methods for energy consumption optimization research in the future IIoT.

Evaluasi kualitas layanan aplikasi Pustabiblia menggunakan metode digiQUAL

Introduction. This research focuses on evaluation of digital library quality services using DigiQUAL method. This research provide evaluation results and knowledge regarding the quality of Pustabiblia application services to support further development. Data Collection Methods. This is quantitative research with descriptive analysis. Data was taken from a sample of 80 respondents. Data Analysis. Data analysis techniques used mean and grand mean with Microsoft Excel application to calculate the average service quality variable. The categorization criteria was conducted. Results and Discussion. The results showed that it was in the high or good category with a service quality index of 3.64. The lowest index in the research was the Resource Use with a value of 3.39. Conclusion. In general, the quality of the library application service is already in the “good” category but it can still be improved to be in the “very good” category. Further, there needs to be promotion and recommendations regarding the use of the library application so that students can access the library application more often, particularly to meet student information needs.

Congestion and throughput optimization protocol for providing better quality of service and experience

<p>Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.</p>

The Influence Of Application Quality And Service Quality On Shopee Customer Satisfaction In Bogor

This study aims to determine: (1) the effect of Application Quality on Shopee Customer Satisfaction in Bogor, (2) the effect of Service Quality on Shopee Customer Satisfaction in Bogor, and (3) The Effect of Application Quality and Service Quality together on Shopee Customer Satisfaction in Bogor. The sample used in this study was 385 samples of Bogor city residents who had shopped or used Shopee. The method used in this research is descriptive quantitative, where theoretical testing is carried out by measuring the research variables numerically and analyzing the data. In this study using primary data obtained directly from respondents who will be collected through a questionnaire. And using SPSS as a tool to solve problems. The questionnaire in this study uses a Likert scale as a measurement scale for research instruments. Based on the descriptive results on each variable, it shows that the independent variables Application Quality (X1) and Service Quality (X2) have a positive and significant effect on the dependent variable Customer Satisfaction (Y). This can be seen from the results of the analysis of the testing of each variable X and Y, both partial testing (t test) and simultaneous testing (f test). Keywords: Application Quality, Service Quality, Customer Satisfaction, Shopee.

UAVs-assisted QoS guarantee scheme of IoT applications for reliable mobile edge computing

Unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) is an emerging network architecture that has been considered as a promising transformative service paradigm. It enhances the coverage of intelligent mobile communication network and promotes the rapid development of a wide range of internet of things (IoT) applications. However, the ever-increasing data transmission demands given rise by these IoT applications based on MEC have posed significant challenges to the service of quality (QoS) guarantee of UAVs-assisted MEC (UAVs-MEC) network. To address the issue, we propose a novel UAVs-MEC QoS guarantee scheme that can enhances the reliability of IoT applications in UAVs-MEC network. Specifically, a dynamic QoS-aware based service level agreement (SLA) compliance verification model for MEC service is proposed in the scheme. It can monitor and detect the consistency of dynamic QoS of IoT applications through UAVs to ensure the continuous SLA compliance of service provided by MEC. Additionally, an objective weight assignment method and an adaptive update method are presented to improve the accuracy and scalability of the scheme. Finally, the simulation experiments conducted using a real-world dataset show that the proposed scheme can efficiently and effectively verify the SLA compliance of MEC service for guaranteeing QoS of IoT applications in UAVs-MEC context while outperforms other existing SLA violation detection methods.

Defining Service Quality of BRI Merchant Application Using Importance Performance Analysis

The COVID-19 pandemic has changed people's habits worldwide, accelerating the adoption of cashless payment transactions, especially in Indonesia. This situation allows Financial Technology companies and Conventional Banks to increase competition. This thesis focuses on the Digital Payments Industry in Indonesia, emphasizing the Merchant Acceptance Business, which has a crucial role as a catalyst for Cashless Payments. This research is based on previous studies regarding service quality, business model canvas, and the importance of performance analysis of the BRI Merchant Application. This research aims to define a service quality strategy for BRI merchant applications through a critical performance analysis approach. The study found that the quality of BRI merchant application services consists of changing merchant data, adding EDC, Checking transactions, reconciling Payments, filing complaints, and monitoring complaints.

A Queue Monitoring System in OpenFlow Software Defined Networks

Real-time trac characteristic is dierent and it is very sensitive to delay. To meet trac specications in real time, monitoring systems are used as an important part of networking. Many monitoring systems are deployed to have an update view of the network QoS parameters and performance. Most of these systems are implemented to measure QoS parameters in links. Here, in this paper, a system for monitoring queues in each link by means of Software Defined Networks is proposed. The monitoring system is implemented by extending Floodlight controller, which uses OpenFlow as southbound protocol. The controller has a centralized view of the network. By the help of OpenFlow it also can provide flow level statistics. Using these advantages, the proposed system can monitor delay and available bandwidth of a queue on a link or path. Despite of monitoring systems in traditional networks, the proposed monitoring system makes a low overhead in network thanks to OpenFlow protocol messages. It is also integrated into the network controller, which enables QoS and trac engineering applications to use the system's reports for automatic trac management and QoS setup. The experimental results show a 99% accuracy of the proposed system for monitoring of both bandwidth and delay.

ISwap: A New Memory Page Swap Mechanism for Reducing Ineffective I/O Operations in Cloud Environments

This paper proposes iSwap, a new memory page swap mechanism that reduces the ineffective I/O swap operations and improves the QoS for applications with a high priority in the cloud environments. iSwap works in the OS kernel. iSwap accurately learns the reuse patterns for memory pages and makes the swap decisions accordingly to avoid ineffective operations. In the cases where memory pressure is high, iSwap compresses pages that belong to the latency-critical (LC) applications (or high-priority applications) and keeps them in main memory, avoiding I/O operations for these LC applications to ensure QoS; and iSwap evicts low-priority applications’ pages out of main memory. iSwap has a low overhead and works well for cloud applications with large memory footprints. We evaluate iSwap on Intel x86 and ARM platforms. The experimental results show that iSwap can significantly reduce ineffective swap operations (8.0% - 19.2%) and improve the QoS for LC applications (36.8% - 91.3%) in cases where memory pressure is high, compared with the latest LRU-based approach widely used in modern OSes.

Persepsi Nasabah terhadap Aplikasi M-Banking pada Bank Swasta dan Bank Pemerintah

Information and communication technology has revolutionized the banking sector and at its peak during the Covid 19 pandemic, most transactions can be carried out without the need to visit a bank and using mobile banking applications. In Indonesia, there are two banks that are most popular in using m-banking, m-BCA which is managed by a private bank and Livin by Mandiri which is managed by a state-owned company. The aim of this research is to analyze whether there are differences of trust, service quality and perceived risk on customer satisfaction in private and government managed m-Banking. The research method uses a quantitative approach based on data collected through online surveys. There are 180 active m-Banking customers with generation Z and Millennial demographics, students and productive workers. The results show that the factors that influence customer satisfaction in using m-Banking lie in the aspects of service quality and trust, while perceived risk has no effect. Furthermore, differences in the influence of m-banking application service quality determine customer satisfaction. Where, m-BCA customers are relatively more satisfied because the service is in accordance with the promises, transaction speed, security, attractive visual appearance, the application is more responsive and easier than the Livin by Mandiri. For generation Z and millennials, the use of digital banking applications is more influenced by the application's ability to meet transaction needs rather than threat factors such as the speed of computing personal data, cyber-crime and bank system errors.

VALIDATING DIMENSIONS OF MOBILE SERVICE QUALITY E-COMMERCE CONSUMER TO CONSUMER (C2C) IN INDONESIA FROM SELLER'S PERSPECTIVE

Along with technological advancement, many sellers in Indonesia have shifted to use digital platform like e-commerce, which is creating potential ecosystem for various e-commerce applications to grow and face intense competition in facilitating direct transactions between consumers (C2C). This study aims to confirm the mobile service quality dimensions of e-commerce on mobile applications. The study adopts a quantitative approach with sellers who are use of popular C2C mobile applications in Indonesia, such as Tokope­dia, Shopee, Bukalapak, and TikTok Shop as the population. Sample selection using purposive technique resulted in a sample size of 313 respondents. Test­ing was conducted using Confirmatory Factor Analysis (CFA) to determine the proposed dimensions of e-commerce mobile application service quality from previous research. The results of the study confirm six factors of mobile service quality in e-commerce applications: Application Design, Reliability, Responsiveness, Trust, Efficiency, and System Availability. Testing with the modified model provides higher fit criteria. ANOVA was used to compare components in each dimension. E-commerce service providers can use the re­sult to improve service quality. These improvements involve refining the ap­plication interface, ensuring reliability, responsiveness, security, efficiency, and system availability, aiming to boost user satisfaction, foster continued app usage, and fortify the service provider's reputation.

A fine-grained robust performance diagnosis framework for run-time cloud applications

To maintain the required service quality of time-critical cloud applications, operators must continuously monitor their runtime status, detect potential performance anomalies, and diagnose the root causes of these anomalies effectively. However, existing performance diagnosis methods face challenges such as the need for high-quality labeled data, the low reusability and robustness of performance anomaly detection models, and the absence of real-time fine-grained root cause localization. These challenges make fixing performance issues quickly and developing effective adaptation decisions difficult. We provide a Fine-grained Robust Performance Diagnosis (FIRED) framework to tackle those challenges. The framework offers a metrics selection component to filter noise and improve detection efficiency, an anomaly detection component that assembles several well-selected base models with a deep neural network, and adopts weakly supervised learning considering fewer labels exist in reality. The framework also employs a real-time, fine-grained root cause localization component to locate dependent resource metrics of performance anomalies. Our experiments show that the framework can effectively reduce data noise and achieve the best accuracy and algorithm robustness for performance anomaly detection. In addition, the framework can accurately localize the first root causes, with an average accuracy higher than 0.7 for locating the first four root cause metrics.