Quality Of Service Framework Research Articles

Quality of Service in Sustainable Development: A QoS-based Analysis of the United Nations Sustainable Development Goals

This paper explores the innovative application of Quality of Service (QoS) principles, traditionally rooted in network and telecommunications, across various sectors to enhance the achievement of the United Nations Sustainable Development Goals (SDGs). By establishing a parallel between QoS in technological systems and sectors such as healthcare, education, energy, and urban development, we examine how these principles can be adapted to improve service delivery and efficiency in critical areas of sustainable development. The core premise is that methodologies used to optimize data flow and service reliability in networks can similarly enhance the quality, accessibility, and sustainability of essential services. This study conducts a comprehensive comparative analysis to illustrate the potential of QoS frameworks in promoting efficiency, effectiveness, and equity across multiple domains of the SDGs. Key areas of focus include enhancing healthcare outcomes, increasing educational accessibility, ensuring sustainable energy use, and improving urban infrastructure. The findings advocate for a cross-disciplinary approach to applying QoS principles, suggesting that such integration can lead to substantial improvements in service delivery and contribute significantly to global sustainable development efforts.

Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework.

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.

Decentralized Resource Allocation for Multi-Radar Systems Based on Quality of Service Framework

Decentralized Resource Allocation for Multi-Radar Systems Based on Quality of Service Framework

QoS Management for XR Traffic in 5G NR: A Multi-Layer System View and End-to-End Evaluation

Extended reality (XR) applications are gaining a lot of momentum in the industry and consumer market. XR applications are characterized by multiple simultaneous data flows with different Quality-of-Service (QoS) requirements, imposing the need for advanced QoS management in 5G networks. Compared to 4G, the 5G NR technology has been designed with enhanced QoS support, by introducing the concept of QoS flows and an additional layer for QoS handling. However, recent studies show that additional QoS enhancements are needed in 5G to support the massive adoption of XR services. 3GPP envisions to include some enhancements for XR traffic in 5G-Advanced. In this paper, we review the current 5G QoS framework and study potential design improvements to fulfill XR QoS requirements in future cellular networks. We study QoS control procedures for XR traffics at different layers of the 5G NR protocol stack, including the proper configuration of radio access and core networks. In particular, we analyze various architectures for handling the peculiarities of XR, such as time-constraint and multi-flow applications. Also, we present XR traffic adaptation mechanisms at the application layer, to properly adapt XR traffic statistics to the actual 5G radio environment, and QoS schedulers for the MAC layer, leading to cross-layer performance optimization between XR application and 5G radio access network. Finally, we provide a validation study and end-to-end evaluation of QoS control mechanisms by simulating realistic mixed traffic scenarios in a 5G NR system-level simulator based on ns-3.

Joint Power and Bandwidth Allocation in Collocated MIMO Radar Based on the Quality of Service Framework

The simultaneous multi-beam working mode of the collocated multiple-input and multiple-output (MIMO) radar enables the radar to track multiple targets simultaneously. A joint power and bandwidth allocation algorithm in a collocated MIMO radar based on the quality of service (QoS) framework is proposed for the multi-target tracking problem with different threat levels. Firstly, a posterior Cramer–Rao lower bound (PCRLB) concerning the power and bandwidth is derived. In addition, the optimal objective functions of power and bandwidth are designed based on the QoS framework, and the problem is solved using the convex relaxation technique and the cyclical minimization algorithm. The numerical results show that the proposed algorithm has better tracking accuracy and achieves more reasonable resource allocation compared to strategies such as average allocation.

Coalition Game of Radar Network for Multitarget Tracking via Model-Based Multiagent Reinforcement Learning

Task assignment is crucial for multitarget tracking of the radar network and is mainly solved by centralized optimization methods, which results in the issues of robustness deficiency, high computational cost, and inflexible adaptability in complex environments. To overcome these issues, task assignment of the radar network for multitarget tracking is formulated as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiagent decision-making and learning</i> problem, where each radar node in the network acts as an intelligent agent that can make the tracking decision according to its task preference and interact with other agents for the sake of the best network utility. To describe the decision references of radar nodes for various tasks, the criterion for the design of the utility function is presented, and a utility function under this criterion is devised based on the quality of service framework. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coalition game with transferrable utility</i> is developed for task assignment, where the utility of the coalition is completely transferred to all coalition members. The existence of the stable coalition partition of the developed game is analyzed theoretically, and the model-based multiagent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random Fourier features reinforcement learning</i> (RFFRL) algorithm is proposed to solve the optimal solution to the game in the high dimensional state space, which is proven to be converged at a Nash-stable coalition partition. Some numerical simulation results are provided to illustrate the effectiveness of the proposed algorithm in terms of tracking performance and resource conservation.

Toward a Data-Driven Cognitive Framework for Adaptive QoS in IoT

Quality of service (QoS) is an increasingly important, stringent, and challenging requirement in the Internet of Things (IoT). Domain and context-dependent circumstances bring forth the need for a cognitive design to facilitate QoS that can learn and adapt as the requirements change and deployments evolve. The performance data that measure QoS metrics based on variations in different communication stack parameters/protocols is becoming available. The research literature highlights the impact of various configurable parameters, such as transmission power (TP) and packet size (PS) on metrics, such as reliability and delay. In this article, we propose the design of a cognitive framework for adaptive QoS in the IoT. The proposed approach is based on performance data from deployments of different types of communication networks forming IoT. The aim is to be able to adaptively configure communication parameters/protocols ensuring that the QoS constraints in evolving network scenarios are met.

Multimedia Priority Service over Wi-Fi Networks

Communication is a crucial capability for people with national security and emergency preparedness (NSEP) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> For example, NSEP Services are available in the United States as Government Emergency Telecommunications Service and Wireless Priority Service (https://www.cisa.gov/priority-telecommunications-services). responsibilities. There are several scenarios where Wi-Fi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Wi-Fi is a brand name and a trademark of Wi-Fi Alliance (https://www.wi-fi.org/). access networks may provide the best or only means for NSEP users to access the global communication infrastructure. However, unless communications traffic associated with NSEP users receives priority access to the wireless medium, it could experience significant performance degradation if the access network, which might be shared with a large number of non-NSEP users, is congested. Realizing this need for priority treatment of NSEP users' traffic, efforts are underway to define a Multimedia Priority Service <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> To support MPS, priority access capabilities are also defined in 3GPP (see TS 22.153: Multimedia Priority Service). (NSEP priority access) within IEEE 802.11be: Enhancement for extremely high throughput (EHT) [1], a next generation amendment of the IEEE 802.11 standard [2]. The NSEP priority access features encompass medium access control layer capabilities for discovery, verification of NSEP authorization, and invocation/revocation of priority access, and a mechanism to provide priority channel access to NSEP users' traffic. They are specified in IEEE 802.11be generically as Emergency Preparedness Communication Service priority access features. The performance study described in this article demonstrates that even in heavily congested Wi-Fi networks it is possible, using a scheme within the quality of service framework specified in IEEE 802.11be, to provide priority to NSEP traffic with limited negative impact on non-priority real-time traffic.

A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices

The prominence of Machine-to-Machine (M2M) communications in the future wide area communication networks place various challenges to the cellular technologies such as the Long Term Evolution (LTE) standard, owing to the large number of M2M devices generating small bursts of infrequent data packets with a wide range of delay requirements. The channel structure and Quality of Service (QoS) framework of LTE networks fail to support M2M traffic with multiple burst sizes and QoS requirements while a bottleneck often arises from the limited control resources to communicate future uplink resource allocations to the M2M devices. Moreover, many of the M2M devices are battery-powered and require a low-power consuming wide area technology for wide-spread deployments. To alleviate these issues, in this article we propose an adaptive semi-persistent scheduling (SPS) scheme for the LTE uplink which caters for multi-service M2M traffic classes with variable burst sizes and delay tolerances. Instead of adhering to the rigid LTE QoS framework, the proposed algorithm supports variation of uplink allocation sizes based on queued data length yet does not require control signaling to inform those allocations to the respective devices. Both the eNodeB and the M2M devices can determine the precise uplink resource allocation related parameters based on their mutual knowledge, thus omitting the burden of regular control signaling exchanges. Based on a control parameter, the algorithm can offer different capacities and levels of QoS satisfaction to different traffic classes. We also introduce a pre-emptive feature by which the algorithm can prioritize new traffic with low delay tolerance over ongoing delay-tolerant traffic. We also build a model for incorporating the Discontinuous Reception (DRX) mechanism in synchronization with the adaptive SPS transmissions so that the UE power consumption can be significantly lowered, thereby extending their battery lives. The simulation and performance analysis of the proposed scheme shows significant improvement over the traditional LTE scheduler in terms of QoS satisfaction, channel utilization and low power requirements of multi-service M2M traffic.

Analysis and evaluation of the effectiveness of methods for ensuring the quality of service for software-defined networks of the standard 5G/IMT-2020

The quality of service (QoS) in networking is the process of managing network resources to reduce packet loss and to lower network jitter and latency. QoS has been widely used in traditional network and can also be implemented in the 5G standard based on a software-defined network (SDN). A traditional network carries several challenges, such as vendor dependency, the complexity of managing a large network, dynamically changing forwarding policies, and more. Software-defined networking is a new networking strategy designed to address the challenges of a traditional IP network, such as high levels of complexity and inability to adapt to the new quality of service requirements in a timely manner. The fundamental idea behind SDNs compared to the conventional networking paradigm is the creation of horizontally integrated systems through the separation of the control and the data plane while providing an increasingly sophisticated set of abstractions. Recently, various SDN-enabled QoS frameworks have emerged that offer many possibilities for network reconfiguration and high-level definition of policies. QoS requirements for 5G networks have been defined on the basis of three main categories of use cases: extreme mobile broadband (xMBB), massive machine type communications (mMTC) IoT/M2M devices, and highly reliable М2М-communication (ultra-reliable machine-type communications – uMTC). This paper analyzes and surveys the QoS based on the openflow protocol method and QoS based on open-source SDN controllers method in 5G network. In addition, we discuss various architectural issues of open-source SDN controllers network and examine their impact on the QoS. Furthermore, we outline the characteristics of the QoS parameters such as latency, availability, reliability, jitter, and bandwidth in the 5G network. Finally, the article discusses and compares parameters of the QoS in 5G determined by world’s leaders in 5G technology.

A Software-Defined Queuing Framework for QoS Provisioning in 5G and Beyond Mobile Systems

There is an ever-increasing demand for network technologies supporting Ultra-Reliable Low Latency Communications (URLLC) services and their co-existence with best-effort traffic. By way of example, reference can be made to the emerging 5G mobile networks. In this vein, this article investigates the Software-Defined Networking (SDN) technology capabilities for providing Quality of Service (QoS) guarantees. Specifically, we present a testbed, under development, dubbed Soft-ware-Defined Queueing (SDQ). This framework leverages QoS provision functionalities of SDN. SDQ can be regarded as a framework for testing traffic engineering solutions in networks with deterministic QoS support. By using SDQ, we develop and test a specific solution that chooses the optimal queue and path for each incoming flow in order to reduce the workload imbalances in the network. For the experimental setup, we consider a generic SDN network whose bridges include three priority queues at every output port. Furthermore, we compare the aforementioned solution with a best-effort network and an SDN-enabled network with QoS support configured by default. The obtained results show that the envisioned solution outperforms the baseline one of SDN and the best-effort solution in terms of the average latency recorded.

Robust Power Allocation for Resource-Aware Multi-Target Tracking With Colocated MIMO Radars

Power allocation is an essential part of the design of colocated multiple-input and multiple-output (C-MIMO) radar systems for multi-target tracking (MTT). A widely adopted power allocation approach is to directly minimize the amount of allocated power while satisfying the desired performance for each tracked object so as to conserve the power resources. However, this approach may not lead to a satisfactory solution when the available power is not sufficient to achieve the desired performance for all the targets. To overcome the limitations of this approach, a robust power allocation (RPA) methodology is proposed in this paper based on the quality of service framework. At its core, the proposed RPA employs a task utility function that quantifies the tracking performance for different power allocations in a flexible manner. The Bayesian Cramér-Rao lower bound (BCRLB) is utilized to formulate the task utility function since it provides a lower bound on the accuracy of target state estimates. By using a set of weights to quantify the importance of different tracked objects, the objective function of RPA is modeled as the weighted sum of task utility functions. This formulation of the RPA problem is demonstrated to be a non-convex optimization problem in general. We show that the task utility function is unimodal. Based on the specific structure of the task utility function, we propose an iterative parallel search algorithm to find the solution. Numerical experiments involving scenarios with both sufficient and insufficient power resources demonstrate the robustness and efficiency of the proposed strategy.

A Lightweight Location-Aware Fog Framework (LAFF) for QoS in Internet of Things Paradigm

Realization of Internet of Things (IoT) has revolutionized the scope of connectivity and reachability ubiquitously. Under the umbrella of IoT, every object which is smart enough to communicate with other object leads to the enormous data generation of varying sizes and nature. Cloud computing (CC) employs centralized data centres for the provisioning of remote services and resources. However, for the reason of being far away from client devices, CC has their own limitations especially for time and resource critical applications. The remote and centralized characteristics of CC often result in creating bottle necks, being latent, and hence deteriorate the quality of service (QoS) in the provisioning of services. Here, the concept of fog computing (FC) emerges that tends to leverage CC and end devices for data congestion and processing locally in a distributed and decentralized way. However, addressing latency and bottleneck issues for time critical applications are still challenging. In this work, a lightweight framework is proposed which employs the concept of fog head node that keeps track of other fog nodes in terms of user registrations and location awareness. The proposed lightweight location-aware fog framework (LAFF) persistently satisfies QoS by providing an accurate location-aware algorithm. A comparative analysis is also presented to analyse network usage, service time, latency, and RAM and CPU utilization. The comparison results depicts that the LAFF reduces latency, network use, and service time by 11.01%, 7.51%, and 14.8%, respectively, in contrast to the state-of-the-art frameworks. Moreover, considering RAM and CPU utilization, the proposed framework supersedes IFAM and TPFC targeting IoT applications. The RAM consumption and CPU utilization are reduced by 8.41% and 16.23% as compared with IFAM and TPFC, respectively, making the framework lightweight. Hence, the proposed LAFF improves QoS while accessing remote computational servers for the outsourced applications in fog computing.

QoS to QoE Mapping Function for Iptv Quality Assessement Based on Kohonen Map: A Pilot Study

Abstract This paper deals with an analysis of Kohonen map usage possibility for real-time evaluation of end-user video quality perception. The Quality of Service framework (QoS) describes how the network impairments (network utilization or packet loss) influence the picture quality, but it does not reflect precisely on customer subjective perceived quality of received video stream. There are several objective video assessment metrics based on mathematical models trying to simulate human visual system but each of them has its own evaluation scale. This causes a serious problem for service providers to identify a critical point when intervention into the network behaviour is needed. On the other hand, subjective tests (Quality of Experience concept) are time-consuming and costly and of course, cannot be performed in real-time. Therefore, we proposed a mapping function able to predict subjective end-user quality perception based on the situation in a network, video stream features and results obtained from the objective video assessment method.

Optimized Framework for QoS Efficient Routing Scheme in Wireless Mesh Network

Adoption of Wireless Mesh Network offers cost effective data transmission system; however, there is always a problems associated with larger scale of deployment where resource consumption is inevitable and beyond control. After reviewing the existing approaches toward improving the routing operation in Wireless Mesh Network, it has been seen that there are still a larger scope in improvement. Therefore, the proposed study introduces an optimized framework for quality of service that jointly works towards resolving hidden terminal problem as well as performs enhance data delivery in presence of challenging traffic scenario. The proposed system uses analytical modeling approach for channel occupancy is formulated and it performs computation of channel capacity. Further, an improved scheduling approach has been formulated in order to ensure superior saving of energy by considering various novel ranges of empirical parameters. Simulated in MATLAB, the proposed system highlights that it offers reduced delay, increased throughput, and highly controlled energy efficiency when compared with the existing routing protocols claims of traffic management in wireless mesh network.

Overview and Framework of Quality Service Metrics for Cloud-Based Robotics Platforms

With the rapid development of big data, cloud computing and other technologies, Cloud-based robotic has become one of the key research directions for service robot, such as used in hospitals. A framework and set of metrics for evaluating the quality of service (QOS) of a cloud robotic platform would be greatly facilitate research into and actual practice of service robots. In this paper, a QOS metrics framework of cloud robotic computing is summarized and the research of components and metrics of a cloud robotic platform is reviewed. QOS metrics are organized into software, network, and robotic services. By summarizing and analyzing the above three groups of metrics, a QOS framework or index system is proposed. Finally, future research towards open source and standardization of components of robotic cloud platform is discussed.

Quality of service-based service selection in smart parking

Smartness in the existing environment is required for overall growth of any country. Government is putting tremendous effort and a huge sum of money for making cities smart to achieve smartness. A very first asset that needs to be made smart is smart parking system to avoid the traffic congestion. There are many service providers which are offering smart parking services, but there is no quality of service (QoS) framework available so far. So, for a customer point of view, it is very hard to select the best service provider and gain maximum satisfaction. So, in this paper, we have designed a QoS framework which consists of 33 metrics to evaluate a smart parking service. These parameters are identified from the user as well as vendors' perspective and help to select better service provider. We have also proposed multi criteria decision making (MCDM) approach technique for order preferences by similarity to ideal solution (TOPSIS) to select best smart parking service provider based on identified QoS. We have demonstrated our approach with the help of case studies.

Quality of service-based service selection in smart parking

Smartness in the existing environment is required for overall growth of any country. Government is putting tremendous effort and a huge sum of money for making cities smart to achieve smartness. A very first asset that needs to be made smart is smart parking system to avoid the traffic congestion. There are many service providers which are offering smart parking services, but there is no quality of service (QoS) framework available so far. So, for a customer point of view, it is very hard to select the best service provider and gain maximum satisfaction. So, in this paper, we have designed a QoS framework which consists of 33 metrics to evaluate a smart parking service. These parameters are identified from the user as well as vendors' perspective and help to select better service provider. We have also proposed multi criteria decision making (MCDM) approach technique for order preferences by similarity to ideal solution (TOPSIS) to select best smart parking service provider based on identified QoS. We have demonstrated our approach with the help of case studies.

A Cross layer QoS Framework for Heterogeneous IoT environment

Internet of Things (IoT) is one of the fast growing technological paradigm in terms of architecture, standards, protocols, infrastructure deployment, Quality of Service (QoS), Service Level Agreements (SLAs), service provisioning, cross domain and cross platform implementations. IoT involves the techniques and technologies for sensing, actuation, communication, computation, networking and storage. In such a demanding environment the need for cross layer QoS functionalities are essential to address the issues like resources, mobility, security and energy management. The detailed review of literatures on IoT architectures and QoS implementations is made and it is observed that there is a need for cross layer QoS model in IoT environments and is one of the critical research challenges. A novel approach to address the above challenge(s) in an IoT environment requires an appropriate lathering of functional modules to different layers to meet different QoS requirements. Hence we propose a novel cross layer QoS framework supporting adaptable and distributed decision making in the IoT environment as a cross layer implementation addressing energy optimization and bandwidth efficiency. The results are verified by implementing the proposed framework in realistic IoT systems for verifying QoS parameters like delay, energy and bandwidth.

QoE Enhancement in Next Generation Wireless Ecosystems: A Machine Learning Approach

Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring quality of service (QoS) will be one of the major challenges on account of a variety of factors that are beyond the control of network and service providers in these environments. In this context, ITU-T is working on defining new Recommendations related to QoS and users' quality of experience (QoE) for the 5G era. Considering the new ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next generation wireless ecosystems taking advantage of big data and machine learning (ML). The methodological approach is based on the use of supervised and unsupervised ML techniques in order to identify both the KQIs relevant for the users and the network performance (NP) anomalies. The proposed methodology links the NP and QoE via inductive ML algorithms and provides information about the areas where corrective actions are required. The results from a case study conducted to validate the model in real-world Wi-Fi deployment scenarios are also presented.