Different Quality Of Service Research Articles

Children with Rare Diseases

Background: China's child welfare system is a the process of transition from compensation-type to moderate inclusive type. However, it still heavily relies on the path of compensation-type. Based on insufficient national governance capabilities, the core of welfare in China is being challenged and it has lead to the dislocation and insufficiency of welfare supply to children with rare diseases. A multi-coordinated national care service system is urgently in need to be developed. Objectives: In the context of state parental power, what is the current situation of welfare of children with rare diseases in China? What are the characteristics? How to play a coordinated role of all stakeholders in the care delivered to children with rare diseases? Methods: The methods of interview, literature review and questionnaire survey were used to understand the basic situation of children with rare diseases and the interaction between the welfare provider and the providing mechanism. Basic information on children with rare diseases, family status and needs were collected. Relevant literature on the protection of children with rare diseases from the central government was analyzed. Results: Fourfold dislocation impedes the provision of resources for the welfare delivered to children with rare diseases. Economically, the government's basic welfare services for children with rare diseases seems insufficient In the healthcare field, the quality of specific services offered by healthcare organizations is uneven. In terms of timing, children with rare diseases face the effects of having few caregivers allocated. Main Contribution to Evidence-Based Practice: This paper analyzes the status quo of welfare services and healthcare delivered to children with rare diseases. It analyzes the causes of difficulties and insufficiency of the welfare services and policies for the children with rare disease and puts together knowledge for evidence-based practice and policy-making for children with rare diseases.

Weighted De-Synchronization Based Resource Allocation in Wireless Networks

Considering the exponential growth of wireless devices with data-starving applications fused with artificial intelligence, the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment. The Kuramoto model is described as nonlinear self-sustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength, in which a mutual behavior is accomplished. In this work, we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network, where each user has different quality of service (QoS) requirements. Because the original Kuramoto model is the synchronization model, we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieve weighted fair resource allocation for users with different QoS requirements. The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner. We analyze the convergence condition for the proposed model, and the results reveal that the proposed algorithm achieves a weighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.

On the Ergodic Rate of Cognitive Radio Inspired Uplink Multiple Access

With the exponential increase of the number of devices in the communication ecosystem toward the upcoming sixth generation (6G) of wireless networks, more enabling technologies and potential wireless architectures are necessary to fulfill the networking requirements of high throughput, massive connectivity, ultra reliability, and heterogeneous quality of service (QoS). In this work, we consider an uplink network consisting of a primary user (PU) and a secondary user (SU) and, by integrating the concept of cognitive radio and multiple access, two protocols based on rate-splitting multiple access and non-orthogonal multiple access with successive interference cancellation are investigated in terms of ergodic rate. The considered protocols aim to serve the SU in a resource block which is originally allocated solely for the PU without negatively affecting the QoS of the PU. We extract the ergodic rate of the SU considering a specific QoS for the PU for the two protocols and perform a qualitative and quantitative comparison between the two protocols. In the numerical results, we validate the theoretical analysis and illustrate the superiority of the considered protocols over two benchmark schemes.

Advances in Survivability-supported Framework for Traffic Engineering in Multi-service Backbone Networks

In this paper, optimisation models and heuristic algorithms that address the off-line survivability-supported Traffic Engineering (TE) problem in multi-service backbone networks are presented. In such networks traffic demands with different Quality of Service (QoS) and survivability requirements (e.g. existence of a node disjoint backup path for each primary path) inhere. The optimisation models for engineering the QoS traffic with different survivability prerequisites and the Best-Effort (BE) traffic are based on novel admission control/routing Integer Linear Programming (ILP) and Linear Programming (LP) optimisation sub-problems, which are solved sequentially. LP relaxations of the ILP sub-problems are also provided. The optimisation models and heuristic algorithms are tested on two different networks and their performance is compared.

Model-based Resource Management for Fine-grained Services

Brief Biography: Alim Ul Gias is currently a Research Associate at the Centre for Parallel Computing (CPC), University of Westminster. He completed his PhD from Imperial College London in 2022. Before starting his PhD, Alim was a lecturer at Institute of Information Technology (IIT), University of Dhaka (DU). He completed his bachelor's and master's program from the same institute. His current research focuses on different Quality of Service (QoS) aspects of cloud-native applications e.g., microservices. In particular, he aims to address the performance and resource management challenges concenrining the microservices architecture.

Learning to Broadcast for Ultra-Reliable Communication With Differential Quality of Service via the Conditional Value at Risk

Learning to Broadcast for Ultra-Reliable Communication With Differential Quality of Service via the Conditional Value at Risk

A Flexible Web Traffic Generator for the dimensioning of a 5G backhaul in NPN

Web-based services have nowadays aggregated the majority of applications, ranging from multimedia delivery to instant messaging, embracing requirements from different classes of users (i.e., gamers, streamers, professionals, etc.). The common factor is the need for connectivity everywhere and every time at high performance and quality, while the services traffic profiles and the users’ behaviour can be drastically different. This need is fully satisfied by the recent 5G mobile networks, where the coexistence of heterogeneous applications with different Quality of Service (QoS) levels is possible, defining new vertical markets and cutting-edge applications. In this context, we propose a Flexible Web Traffic Generator (FWTG) tool to model the dynamics of recent applications and user interactions in a realistic way, allowing the generation of real-time HTTP traffic and the possibility to inject it into real networks. FTWG represents a valid in-laboratory solution for the rapid configuration and applications performance evaluation of network slices in the context of 5G Non-Public Networks (NPN), pursuing an effective emulation approach. The advantage of this approach is that it allows the evaluation of real traffic response to different network configurations (and vice-versa), using components, equipment and network functions of the target network under test. After the description of the rationale and the methodology for defining FWTG models and their implementation, we defined a set of Key Performance Indicators (KPI) generated by the tool (e.g., number of requested objects and size, request rate, throughput, etc.). Then, we present initial validation tests on a Linux-based testbed representing the 5G-based scenarios of interest, emulating also a satellite backhaul as representative of a use-case requiring cost-effective dimensioning. Numerical outcomes of running FWTG in different configurations are then provided, considering a variation of the number of users sharing the same resources, their composition and traffic type, showing with concrete examples the usefulness and flexibility of FWTG in supporting 5G network configuration optimizations. Definitively, the tool is released as Open Source software on Gitlab.

Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges

Continuous integration enables the development of microservices-based applications using container virtualization technology. Container orchestration systems such as Kubernetes, which has become the de facto standard, simplify the deployment of container-based applications. However, developing efficient and well-defined orchestration systems is a challenge. This article focuses specifically on the scheduler, a key orchestrator task that assigns physical resources to containers. Scheduling approaches are designed based on different Quality of Service (QoS) parameters to provide limited response time, efficient energy consumption, better resource utilization, and other things. This article aims to establish insight knowledge into Kubernetes scheduling, find the main gaps, and thus guide future research in the area. Therefore, we conduct a study of empirical research on Kubernetes scheduling techniques and present a new taxonomy for Kubernetes scheduling. The challenges, future direction, and research opportunities are also discussed.

Learning to Broadcast for Ultra-Reliable Communication With Differential Quality of Service via the Conditional Value at Risk

Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst channel conditions. Targeting ultra-reliable communication scenarios, this paper takes a complementary approach by introducing the conditional value-at-risk (CVaR) rate as the expected rate of a worst-case fraction of clients. To support differential quality-of-service (QoS) levels in this class of clients, layered division multiplexing (LDM) is applied, which enables decoding at different rates. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled offline. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and the sample complexity is shown to increase as the designated fraction of worst-case clients decreases. Considering this theoretical result, meta-learning is introduced as a means to reduce sample complexity by leveraging data from previous deployments. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves a CVaR rate close to that achieved when the transmitter knows the fading distribution; and that meta-learning can significantly reduce data requirements.

Antecedents and consequences of brand equity: Evidence from Starbucks coffee brand

This study aims to investigate the effects of coffee brand service quality and customer experience on brand equity, brand personality, and brand reputation. To achieve this purpose, the proposed research model was examined with structural equation modeling using R. Empirical analysis indicates that brand personality and brand reputation are determined by brand equity, which is formed by customer experience explained by service quality. Specific service quality factors that affect customer experience are interaction and result qualities. However, physical environmental quality did not have a statistically significant effect on customer experience. Analysis results suggest that the personality, reputation, and equity that customers have in a coffee brand can be enhanced by customer experience through service quality. The results of this research support the idea of needing to compete with strong brands and how marketing and management tactics around service quality and customer experience can help a company compete.

Downlink network-coded multiple access with diverse power

Normally, in the downlink Network-Coded Multiple Access (NCMA) system, the same power is allocated to different users. However, equal power allocation is unsuitable for some scenarios, such as when user devices have different Quality of Service (QoS) requirements. Hence, we study the power allocation in the downlink NCMA system in this paper, and propose a downlink Network-Coded Multiple Access with Diverse Power (NCMA-DP), wherein different amounts of power are allocated to different users. In terms of the Bit Error Rate (BER) of the multi-user decoder, and the number of packets required to correctly decode the message, the performance of the user with more allocated power is greatly improved compared to the Conventional NCMA (NCMA-C). Meanwhile, the performance of the user with less allocated power is still much better than NCMA-C. Furthermore, the overall throughput of NCMA-DP is greatly improved compared to that of NCMA-C. The simulation results demonstrate the remarkable performance of the proposed NCMA-DP.

Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network

IEEE 802.1 Time-Sensitive Networking (TSN) assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data in handling time-critical traffic. TSN handles different quality of service (QoS) requirements and frame preemption is one of the key features of TSN. In the healthcare sector networking technology preferred by large organizations uses an enormous number of nodes, and thereby, the complexity of the network increases. Since the priority of the medical data varies at times based on the patient's health, dynamic traffic scheduling mechanisms are preferred. To improve the efficiency of the network, the software-defined access mechanism is used to control the network switches and bridges in the time-sensitive network. This work uses reinforcement learning to identify and eliminate the bridges dropping packets, and the alternative path is used to schedule the real-time data traffic. It is perceived that it performs well for the time-critical data in congestion network, increases the throughput, and reduces latency.

Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Investigation of Outpatient Satisfaction in a General Hospital: The Effect of Socio-Demographic Factors

Introduction: Measuring patient satisfaction with health services in recent decades has been an important tool for assessing the quality of specific services. Purpose: The investigation of the degree of satisfaction of patients attending the Regular Outpatient Clinics of a General Hospital as well as the effect of socio-demographic factors on their satisfaction. Method: For the gathering of research data, a structured questionnaire with "closed" questions was used regarding the patients' satisfaction with the reception - environment, speed of service, clinical and laboratory examinations, impressions from the medical care, impressions from the nursing care, the administrative services and the general service. Results: The study involved 36 men (57.1%) and 27 women (42.9%) with a mean age of 56.78 years. The majority of them were insured (79.4%), Greek citizens (90.5%) and high school graduates (36.5%). Also, most outpatients evaluated very satisfactorily the services provided by the Regular Outpatient Clinics of the Hospital. Finally, there was a statistically significant correlation between socio-demographic variables (age, insurance and nationality) and patient satisfaction level (p <0.05).Conclusions: Based on the findings of this research, there is a clear effect of socio-demographic factors on the degree of satisfaction of outpatients.

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security.

Addressing the recent trend of the massive demand for resources and ubiquitous use for all citizens has led to the conceptualization of technologies such as the Internet of Things (IoT) and smart cities. Ubiquitous IoT connectivity can be achieved to serve both urban and underserved remote areas such as rural communities by deploying 5G mobile networks with Low Power Wide Area Network (LPWAN). The current architectures will not offer flexible connectivity to many IoT applications due to high service demand, data exchange, emerging technologies, and security challenges. Hence, this paper explores various architectures that consider a hybrid 5G-LPWAN-IoT and Smart Cities. This includes security challenges as well as endogenous security and solutions in 5G and LPWAN-IoT. The slicing of virtual networks using software-defined network (SDN)/network function virtualization (NFV) based on the different quality of service (QoS) to satisfy different services and quality of experience (QoE) is presented. Also, a strategy that considers the implementation of 5G jointly with Weightless-N (TVWS) technologies to reduce the cell edge interference is considered. Discussions on the need for ubiquity connectivity leveraging 5G and LPWAN-IoT are presented. In addition, future research directions are presented, including a unified 5G network and LPWAN-IoT architecture that will holistically support integration with emerging technologies and endogenous security for improved/secured smart cities and remote areas IoT applications. Finally, the use of LPWAN jointly with low earth orbit (LEO) satellites for ubiquitous IoT connectivity is advocated in this paper.

New Algorithm for a Fixed Right Shift Code to Support Different Quality of Services in Smart and Sustainable Optical Networks

Smart and sustainable cities require a network that can ensure many exchanges of information. In this sense, the deployment of optical fiber seems essential in order to guarantee urban interconnection. In this work, a new algorithm for the right shift code to be used in optical fiber communication (OFC) is proposed. The algorithm was designed to support different quality of services (audio, video, and data) for smart cities. This algorithm was based on varying the code weight for a certain number of users and transmitting a service to them. This algorithm was used for a spectral amplitude-coding optical code division multiple-access (SAC-OCDMA) system. Two detection scheme approaches were used, which are the single photodiode (SPD) detection technique and the direct detection (DD) technique. The performance was analytically studied for the proposed system in terms of BER, SNR, and the maximum number of users. The obtained results, when our system used SPD, showed an improvement in the number of users, about 2.5% at 622 Mbps and 1.4% at 1.25 Gbps and 2.5 Gbps. It can support a number of users up to 151 for video services at BER = 10−12, 300 for audio services with BER = 10−3, and 162 for data services at BER = 10−9 at a data rate of 1.25 Gbps. Furthermore, the system was simulated and showed transmission distances of 30 km at 1.25 Gbps and 6 km at 2.5 Gbps for video services, 80 km at 1.25 Gbps and 30 km at 2.5 Gbps for audio services, and 44 km at 1.25 Gbps and 13 km at 2.5 Gbps for data services. Our proposed system is suggested to be implemented in a fixed fifth-generation passive optical network (F5G PON).

Adaptive multi‐layer clustering strategies based on capacity weight for Internet of Things

SummaryDue to the requirements brought by diversification of IoT applications, differentiation of nodes' capability, dynamic communication environment and demands, the real‐time information of nodes (actual energy consumption, living nodes' density, pairwise nodes' communication radius) should be considered comprehensively for the clustering strategies of wireless sensor networks to achieve efficient, stable, and flexible performance with limited energy and different quality of service (QoS). This article proposes an improved dynamic multi‐layer clustering strategy for various IoT applications with heterogeneous nodes' energy, unpredictable or fast‐changing distribution of alive nodes, and dynamic scenarios. In addition, an adaptive adjustment strategy based on capability weight for multi‐layer clustering network is proposed to reduce the impact of unreasonable head selection cycle of clustering. By analyzing the node energy, the change of node locations and historical data transmission of cluster head, different capability weights are assigned to each node to adaptively re‐cluster the clusters with heavy load and poor performance, further make the network topology better match current situation and specified QoS requirements. Experimental results have demonstrated that proposed strategy can achieve less energy consumption, longer network lifetime, and better load balancing, especially for the cases with heterogeneous initial energy, nonuniform distribution, and higher density of nodes.

A secure trust-based protocol for hierarchical routing in wireless sensor network

<p><span>Wireless sensor networks (WSNs) became the backbone of the internet of things (IoT). IoT applications are vital and demand specific quality of service (QoS) requirements. In addition, security has become a primary concern to provide secure communication between wireless nodes, with additional challenges related to the node’s computational resources. Particular, the design of secure and resource efficient routing protocol is a critical issue in the current deployment of WSNs. Therefore, this paper proposes a novel secure-trust aware routing protocol (ST2A) that provides secure and reliable routing. The proposed protocol establishes communication routes based on calculated trust value in joint with a novel cluster head selection algorithm in the hierarchical routing process. The proposed trust-aware routing algorithm improves the routing security in WSN and optimizes many performance metrics related to WSNs unique characteristics. The results of simulation validate the feasibility of the proposed algorithm for enhancing the network lifetime up to 18% and data delivery by 17% as compared with some state-of-the-art routing algorithms.</span></p>

Dynamic QoS Prediction Algorithm Based on Kalman Filter Modification.

With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments.

An Integrated Framework for More Efficient Web Services Selection Using an Improved Fuzzy AHP

With the rapid development of Cloud Computing and Service Oriented Computing, the process of selecting web services which gives the same functionality with different quality of service (QoS) become an important issue. To deal with the large number of Web services candidates, K-representative Skyline is appeared as a Skyline variant to find the short list of the most relevant Web services that represent a summary about the full skyline Web services result. However, it returns generally a conflicting result. To rank-order K-representative Skyline Web Services, we propose an improved Fuzzy AHP which allows to: i) elicit the QoS importance level using linguistic terms based on natural language, asking fewer efforts to users, ii) reduce the number of inputs and generate automatically all pair-wise matrix of Skyline Web services with respect to each attribute. The experimental evaluation conducted on real world dataset illustrates the feasibility and the effectiveness of our framework in comparison with existing works.