A variety of communication networks, such as industrial communication systems, have to provide strict delay guarantees to the carried flows. Fast and close to optimal quality of service (QoS) routing ...algorithms, e.g., delay-constrained leastcost (DCLC) routing algorithms, are required for routing flows in such networks with strict delay requirements. The emerging software-defined networking (SDN) paradigm centralizes the network control in SDN controllers that can centrally execute QoS routing algorithms. A wide range of QoS routing algorithms have been proposed in the literature and examined in individual studies. However, a comprehensive evaluation framework and quantitative comparison of QoS routing algorithms that can serve as a basis for selecting and further advancing QoS routing in SDN networks is missing in the literature. This makes it difficult to select the most appropriate QoS routing algorithm for a particular use case, e.g., for SDN controlled industrial communications. We close this gap in the literature by conducting a comprehensive up-to-date survey of centralized QoS routing algorithms. We introduce a novel four-dimensional (4D) evaluation framework for QoS routing algorithms, whereby the 4D correspond to the type of topology, two forms of scalability of a topology, and the tightness of the delay constraint. We implemented 26 selected DCLC algorithms and compared their runtime and cost inefficiency within the 4D evaluation framework. While the main conclusion of this evaluation is that the best algorithm depends on the specific sub-space of the 4D space that is targeted, we identify two algorithms, namely Lagrange relaxation-based aggregated cost (LARAC) and search space reduction delay-cost-constrained routing (SSR+DCCR), that perform very well in most of the 4D evaluation space.
In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by ...intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.
Developing highly efficient routing protocols for vehicular ad hoc networks (VANETs) is a challenging task, mainly due to the special characters of such networks: large-scale sizes, frequent link ...disconnections, and rapid topology changes. In this paper, we propose an adaptive quality-of-service (QoS)-based routing for VANETs called AQRV. This new routing protocol adaptively chooses the intersections through which data packets pass to reach the destination, and the selected route should satisfy the QoS constraints and fulfil the best QoS in terms of three metrics, namely connectivity probability, packet delivery ratio (PDR), and delay. To achieve the given objectives, we mathematically formulate the routing selection issue as a constrained optimization problem and propose an ant colony optimization (ACO)-based algorithm to solve this problem. In addition, a terminal intersection (TI) concept is presented to decrease routing exploration time and alleviate network congestion. Moreover, to decrease network overhead, we propose local QoS models (LQMs) to estimate real time and complete QoS of urban road segments. Simulation results validate our derived LQM models and show the effectiveness of AQRV.
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite important for network optimization. The current 5G and conceived 6G network in the future with ultra high ...density, bandwidth, mobility and large scale brings urgent requirement of high efficient end-to-end optimization methods. The conventional network optimization methods without learning and intelligent decision ability are hard to handle the high complexity and dynamic scenarios of 6G. Recently, machine learning based QoS and QoE aware network optimization algorithms emerge as a hot research area and attract much attention, which is widely acknowledged as the potential solution for end-to-end optimization in 6G. However, there are still many critical issues of employing machine learning in networks, especially in 6G. In this paper, we give a comprehensive survey on the recent machine learning based network optimization methods to guarantee the end-to-end QoS and QoE. To easy to follow, we introduce the investigated works following the end-to-end transmission flow from network access, routing to network congestion control and adaptive steaming control. Then we discuss some open issues and potential future research directions.
Workflow management systems (WfMSs) have been used to support various types of business processes for more than a decade now. In workflows or Web processes for e-commerce and Web service ...applications, suppliers and customers define a binding agreement or contract between the two parties, specifying quality of service (QoS) items such as products or services to be delivered, deadlines, quality of products, and cost of services. The management of QoS metrics directly impacts the success of organizations participating in e-commerce. Therefore, when services or products are created or managed using workflows or Web processes, the underlying workflow engine must accept the specifications and be able to estimate, monitor, and control the QoS rendered to customers. In this paper, we present a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes. We also present the implementation of our QoS model for the METEOR workflow system. We describe the components that have been changed or added, and discuss how they interact to enable the management of QoS.
With the ever increasing spectrum demand of broadband multimedia services, cognitive satellite terrestrial networks have emerged as a promising paradigm for future space information networks. To ...provide services with diverse delay quality-of-service (QoS) requirements in an energy-limited system, in this paper, we investigate energy efficient power allocation for cognitive satellite terrestrial networks. Employing statistical delay-QoS metric, power allocation schemes are formulated as optimization problems to maximize effective energy efficiency of secondary satellite communications while satisfying interference constraints imposed by primary terrestrial communications. Specifically, allowing for the availability of instantaneous channel state information (CSI) of the secondary transmitter-primary receiver link, optimal transmit powers are derived for both the cases of statistical and instantaneous interference constraints. Moreover, to provide a theoretical insight on the performance of the considered network, we derive closed-form expressions for the outage probability based on the obtained optimal transmit powers. The simulation results demonstrate the validity of the theoretical results and show the impacts of the delay exponent, interference constraint, and aggregate interference from terrestrial networks on the performance of satellite networks.
To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions. This ...brings high computing overheads and long processing delays. In this work, we develop a deep learning framework to approximate the optimal resource allocation policy that minimizes the total power consumption of a base station by optimizing bandwidth and transmit power allocation. We find that a fully-connected neural network (NN) cannot fully guarantee the QoS requirements due to the approximation errors and quantization errors of the numbers of subcarriers. To tackle this problem, we propose a cascaded structure of NNs, where the first NN approximates the optimal bandwidth allocation, and the second NN outputs the transmit power required to satisfy the QoS requirement with given bandwidth allocation. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks. Simulation results validate that the cascaded NNs outperform the fully connected NN in terms of QoS guarantee. In addition, deep transfer learning can reduce the number of training samples required to train the NNs remarkably.
In this paper, we study the achievable link-layer rate, namely, effective capacity (EC), under the per-user statistical delay quality-of-service (QoS) requirements, for a downlink non-orthogonal ...multiple access (NOMA) network with M users. Specifically, the M users are assumed to be divided into multiple NOMA pairs. Conventional orthogonal multiple access (OMA) then is applied for inter-NOMA-pairs multiple access. Focusing on the total link-layer rate for a downlink M-user network, we prove that OMA outperforms NOMA when the transmit signal-to-noise ratio (SNR) is small. On the contrary, simulation results show that NOMA prevails over OMA at high values of SNR. Aware of the importance of a two-user NOMA network, we also theoretically investigate the impact of the transmit SNR and the delay QoS requirement on the individual EC performance and the total link-layer rate for a two-user network. Specifically, for delay-constrained and delay-unconstrained users, we prove that for the user with the stronger channel condition in a two-user network, NOMA prevails over OMA when the transmit SNR is large. On the other hand, for the user with the weaker channel condition in a two-user network, it is proved that NOMA outperforms OMA when the transmit SNR is small. Furthermore, for the user with the weaker channel condition, the individual EC in NOMA is limited to a maximum value, even if the transmit SNR goes to infinity. To confirm these insightful conclusions, the closed-form expressions for the individual EC in a two-user network, by applying NOMA or OMA, are derived for both users and then confirmed using Monte Carlo simulations.
Nonorthogonal multiple access (NOMA) represents a paradigm shift from conventional orthogonal multiple-access (MA) concepts and has been recognized as one of the key enabling technologies for ...fifth-generation mobile networks. In this paper, the impact of user pairing on the performance of two NOMA systems, i.e., NOMA with fixed power allocation (F-NOMA) and cognitive-radio-inspired NOMA (CR-NOMA), is characterized. For F-NOMA, both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F-NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive. For CR-NOMA, the quality of service (QoS) for users with poorer channel conditions can be guaranteed since the transmit power allocated to other users is constrained following the concept of cognitive radio networks. Because of this constraint, CR-NOMA exhibits a different behavior compared with F-NOMA. For example, for the user with the best channel condition, CR-NOMA prefers to pair it with the user with the second best channel condition, whereas the user with the worst channel condition is preferred by F-NOMA.
Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. ...This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS)-in terms of faster responsiveness and data-specific complex analytics-has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud-based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation-based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation <inline-formula> <tex-math notation="LaTeX">{e} </tex-math></inline-formula>-healthcare systems.