Quality Of Service Guarantees Research Articles

A Performance Analysis of Security Protocols for Distributed Measurement Systems Based on Internet of Things with Constrained Hardware and Open Source Infrastructures.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

Autonomous integration of TSN-unaware applications with QoS requirements in TSN networks

Modern industrial networks transport both best-effort and real-time traffic. Time-Sensitive Networking (TSN) was introduced by the IEEE TSN Task Group as an enhancement to Ethernet to provide high quality of service (QoS) for real-time traffic. In a TSN network, applications signal their QoS requirements to the network before transmitting data. The network then allocates resources to meet these requirements. However, TSN-unaware applications can neither perform this registration process nor profit from TSN’s QoS benefits.The contributions of this paper are twofold. First, we introduce a novel network architecture in which an additional device acts as a central user configuration (CUC) for TSN-unaware applications and autonomously signals their QoS requirements to the network. Second, we propose a processing method to detect real-time streams in a network and extract the necessary information for the TSN stream signaling. It leverages a Deep Recurrent Neural Network (DRNN) to detect periodic traffic, extracts an accurate traffic description, and uses traffic classification to determine the source application. As a result, our proposal allows TSN-unaware applications to benefit from TSNs QoS guarantees. Our evaluations underline the effectiveness of the proposed architecture and processing method.

Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

Network Calculus Theory and Application: A New Graduate Course for Engineering Students

With the development of modern networks, the quality of service guarantee has become even more important than before. However, various new types of traffic and heterogeneous network architecture make it quite challenging to evaluate the quality of service, such as delay and reliability. Therefore, a novel analysis method is needed to solve this situation. Network calculus is a theory used to analyze the queueing problems in networks, which is the main content to be taught in this graduate course. Before network calculus theory is introduced, the classical queueing theory will be taught. Then, the mathematical fundamentals of network calculus, such as min-plus algebra and cumulative arrival/service process, will be introduced. Next, two branches of network calculus, i.e., deterministic network calculus and stochastic network calculus, will be introduced separately, including the definition of deterministic/stochastic arrival curve and service curve, as well as important theorems, such as delay theorem, flow aggregation theorem, server concatenation theorem. After the theoretical knowledge, practical content will be taught in two parts. First, students are encouraged to establish system models based on their graduate research topics, analyze the service guarantee of their models, and present their work in class. Second, a traffic data collection platform will be taught, and students will collect various traffic data and analyze traffic characteristics based on network calculus theory. Based on the feedback from students during the past three years, students are satisfied with this new course because it focuses on frontier technology and the teaching activities are well-organized.

Optimized QoS Routing in Software-Defined In-Vehicle Networks

To address the problems of low network centralized management, weak interaction, limited hardware scalability, compatibility issues and challenges in expansion in the static deployment of traditional in-vehicle networks (IVNs), a new IVN architecture is designed. At the same time, to better meet the IVN Quality of Service (QoS) requirements and improve the real-time guarantee of data transmission, the QoS routing optimization mechanism under the new architecture is established. First, a new IVN architecture including a forwarding plane, control plane and application plane is designed by introducing software defined network (SDN) technology and combining it with the IVN itself. Second, the end-to-end delay optimization model of IVN is established by introducing network calculus theory, defining system parameters, creating a network model, calculating latency, considering queuing and congestion. the traditional routing algorithm is improved, and the DBROA algorithm has been proposed, which enhances the performance of QoS routing by introducing features such as distributed routing decisions, beacon mechanisms, optimization algorithms, and adaptability. This improvement allows it to better meet the QoS requirements of various applications and services, thereby enhancing existing QoS routing algorithms. Finally, an IVN routing optimization system is built and implemented, and the performance of different algorithms is compared and analyzed. The experimental results show that compared with the traditional Dijkstra and ECMP algorithms, the DBROA algorithm can effectively reduce the data forwarding delay and packet loss rate, improve the overall performance of IVN, and provide better QoS guarantees for IVN real-time data transmission.

Restaurant containment measures and perceived service quality: implications for future pandemics

PurposeThis study aims to identify the impact of restaurant pandemic mitigation measures on perceived service quality.Design/methodology/approachA total of 15,251 reviews were collected from 300 TripAdvisor Lisbon restaurant Web pages between March 2020 and December 2021, when Covid-19 contingency measures were in place. Grounded on the online reviews, a word frequency matrix was created and used as input on partial least squares–structural equation modeling to test the proposed hypotheses.FindingsThe results suggest that precaution measures, such as the Vaccination Certificate, negative tests and restaurant layout configuration, positively influenced perceived service quality and consumer satisfaction, moderated by consumers’ beliefs.Originality/valueThis study provides relevant information for restaurant managers, which will help them implement strategies to guarantee service quality, consumer satisfaction and revisit intentions in future pandemic scenarios.

Analysis of Factors Affecting Customer Satisfaction in the Context of Online Shopping in Pakistan

The purpose of this research is to explore the relationships between product quality, service quality, trust, delivery guarantee, and customer satisfaction in the context of online shopping in Pakistan. The research philosophy adopted for this study is positivism, with a quantitative research design using deductive approach. The sample design is non-probabilistic, and convenience sampling was used to select the participants. The sample size of 202 respondents was determined using heuristic sampling. Partial Least Square Structural Equation Modeling (PLS-SEM) was employed as the statistical technique for data analysis. The study found that product quality, service quality, trust, and delivery guarantee significantly influenced customer satisfaction. Moreover, trust mediated the relationship between product quality and customer satisfaction, as well as the relationship between service quality and customer satisfaction. This study provides valuable insights into the factors that contribute to customer satisfaction in the context of online shopping in Pakistan. Moreover, it contributes to the literature on the relationship between trust and customer satisfaction by exploring the mediating role of trust in the context of online shopping.

Reliability-Aware Task Processing and Offloading for Data-Intensive Applications in Edge computing

With the growing popularity of mobile edge computing (MEC), a number of data-intensive applications have been deployed. Quality of Service (QoS), as one of the most important requirements in MEC, has attracted significant attention of both academia and industry. There are two challenges for ensuring the Qos of the data-intensive services. Firstly, besides performance, reliability is another important concern especially for some critical applications, which remains unexplored. Secondly, data compression has to be involved for reducing the heavy workload of wireless communications at the edge of the network. To overcome these challenges, this paper jointly studies the reliability-aware data compression and task offloading for data-intensive applications in MEC. Markov models are constructed to capture the dynamics of the state transitions of MEC systems, and quantitative analyses are conducted for performance and reliability evaluation. For fully taking advantages of data compression in QoS guarantee, we formulate an optimization problem with the objective of minimizing latency while satisfying constraints on reliability and energy consumption. After describing the NP-Hardness of the problem, we apply the techniques of problem linearization and constraint relaxation, transform the original problem into a convex optimization problem. Then, the problem can be solved in a parallel way by introducing the Alternating Direction Multiplier Method (ADMM), and a distributed Reliability-aware Task Processing and Offloading (RTPO) algorithm is presented. Finally, extensive simulation experiments are conducted to validate the efficacy of our approach, the experimental results illustrate the superiority of our approach over both baseline and state-of-the-art algorithm.

Analysis of Optical Camera Communications for Terminal Jiggle With Quality of Service Guarantees

Although Optical Camera Communications (OCC) has carved out a space in sixth-generation(6 G) communications, building a theoretical analysis model for OCC remains an open question. Hence, we propose the analytical models for OCC systematic evaluation with unique quality of service (QoS) requirements, and build a practice communication system to validate the effectiveness of our models. A three-dimension discrete-time Markov chain is modeled to characterize the jiggle of user terminal in actual OCC scenarios. Supporting the randomly-oriented receiver, the available maximum data rate in guaranteeing statistical QoS based on effective capacity theory is derived. Moreover, a receiver imaging model is built to represent the logical relations of optical parameters, which provides guidance for image demodulation. Finally, extensive field experiments are implemented to verify the reasonability of our theoretical OCC models.

Ultra reliability and massive connectivity provision in integrated internet of military things (IoMT) based on tactical datalink

One of the major challenges arising in internet of military things (IoMT) is accommodating massive connectivity while providing guaranteed quality of service (QoS) in terms of ultra-high reliability. In this regard, this paper presents a class of code-domain nonorthogonal multiple accesses (NOMAs) for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system (JTIDS). In the considered scenario, a satellite equipped with Nr antennas servers K devices including vehicles, drones, ships, sensors, handset radios, etc. Nonorthogonal coded modulation, a special form of multiple input multiple output (MIMO)-NOMA is proposed. The discussion starts with evaluating the output signal to interference-plus-noise (SINR) of receiver filter, leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered. The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved. The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying (MSK) and Gaussian MSK (GMSK), two pivotal modulation formats in IoMT standards such as Link-16 and JITDS. Numerical results show that near capacity performance is offered. Fortunately, the performance is obtained using simple forward error corrections (FECs) of higher coding rate than existing schemes do, while the transmit power is reduced by 6 dB. The proposed design finds wide applications not only in IoMT but also in deep space communications, where ultra reliability and massive connectivity is a keen concern.

The intervening influence of the quality of health-care administrative process on the relationship between facilities management service quality and patients’ health-care experience in Ghana

PurposePatient-centred strategies should be applied in health-care facilities management (HcFM) to guarantee service quality to meet patients’ needs and ensure quality patient health-care experience. This paper aims to examine the intervening influence of the quality of health-care administrative process (QAP) on the association between health-care facilities service quality and patients’ experiences with medical care.Design/methodology/approachA quantitative technique was used for this cross-sectional study in three Ghanaian teaching hospitals. A total of 622 relevant questionnaires were used for the analysis of the study using SEM-PLS.FindingsThe intervening influence of the QAP on the relationship between HcFM service quality (empathy and tangibility) and patients’ health-care experience (PHcE) were reinforced whilst that of reliability, responsiveness and assurance were not reinforced. The association between the QAP and PHcE was also established.Research limitations/implicationsA high-quality health-care workforce (both core and supporting) and quality work environment provided by the FM department and QAP are essential during quality-of-care delivery, to reduce threats to patient safety to achieve exceptional PHcE. The constraint on the study is that information was gathered from only Ghana. Hence, the generalisation of the findings will be a challenge. Thus, in future, it is proposed that a comparative study across a developed country and a developing country can be conducted. Future research can assess the influence of the health-care internal appearance on patients’ satisfaction.Practical implicationsPractically, the administrative system can be improved by reducing patients overall waiting time. Steps must also be taken to reduce the problem of needless administrative tasks and practices to simplify administrative practices and improve patients’ total health-care experience (core health-care delivery and HcFM), as this influence patients’ total health-care experience.Originality/valueTo the best of the authors’ knowledge, this empirical validation is one of the initial studies in service quality and FM to examine how health-care administrative process quality affects the relationship between FM service quality and patients’ experiences with medical care. This framework can be adapted for research in different countries to extend knowledge.

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.

Energy‐efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads

AbstractCloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription‐based computing services anytime, anywhere, on a pay‐as‐you‐go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world‐wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for the future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy‐efficient and environmentally sustainable while continuing to deliver high‐quality services. In this article, we propose a vision for learning‐centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this article, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy‐efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability.

Power Control With QoS Guarantees: A Differentiable Projection-Based Unsupervised Learning Framework

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users’ quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user interference channel, where the objective is to maximize the network sum-rate under users’ minimum data rate or QoS requirements and power budget constraints. Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued. The first is called Deep Implicit Projection Network (DIPNet), and the second is called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a differentiable convex optimization layer to implicitly define a projection function. On the other hand, DEPNet uses an explicitly-defined projection function, which has an iterative nature and relies on a differentiable correction process. DIPNet requires convex constraints; whereas, the DEPNet does not require convexity and has a reduced computational complexity. To enhance the sum-rate performance of the proposed models even further, Frank-Wolfe algorithm (FW) has been applied to the output of the proposed models. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing DNNs. The proposed solutions outperform the classic optimization methods in terms of computation time complexity.

Clustered Cell-Free Networking: A Graph Partitioning Approach

By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. In addition, with massive multiple-input-multiple-output (MIMO) antenna arrays employed at BSs, the beamspace channel is sparse for each user, and thus there is no need to serve all the users in a cell by all the beams therein jointly. Therefore, it is of paramount importance to develop a flexible clustered cell-free networking scheme that can decompose the whole network into a number of weakly interfered small subnetworks operating independently and in parallel. Given a per-user rate constraint for service quality guarantee, this paper aims to maximize the number of decomposed subnetworks so as to reduce the signaling overhead and system complexity as much as possible. By formulating it as a bipartite graph partitioning problem, a rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network with simple beam allocation to a fully cooperative network by increasing the required per-user rate. Simulation results demonstrate that the proposed RC-NetDecomp algorithm outperforms existing baselines in terms of average per-user rate, fairness among users and energy efficiency.

Fuzzy logic based dynamic wavelength and bandwidth allocation for 5G front haul networks

The Quality of Service (QoS) requirements for 5G are very strict in terms of latency of Fifth Generation (5G) traffic. 5G fronthaul networks between the baseband unit and remote radio heads are planned to use Next Generation - Passive Optical Networks 2 (NG-PON2) to carry their traffic. However, these Time Wavelength Division Multiplexing (TWDM) PON based networks also provide service to other traffic such as residential and corporate users. Therefore, it is crucial to provide QoS guarantees to 5G traffic. The main issue includes allocating transmission over multiple wavelengths while maintaining QoS. Although using all the available wavelengths all the time may seem to be the simple solution, each active wavelength increases the cost of operation. To solve these issues, this paper proposes a technique using fuzzy logic to dynamically allocate wavelength and bandwidth for 5G fronthaul networks. Two types of traffic, i.e., 5G traffic and normal traffic are considered while the problem of Dynamic Wavelength and Bandwidth Allocation (DWBA) is formulated as a queueing theory problem using penalties and a cost function. Penalties are assigned for waiting packets as well as each active wavelength. Then, modeling the number of active wavelengths, the number of packets in the system, the total cost, and the traffic intensity as input fuzzy variables while modeling the change in the number of active wavelengths as the fuzzy output variable. The Mamdani implication is used for the fuzzy inference engine and the height method is used for defuzzification of the output variable. Simulations in MATLAB show that the proposed technique can maintain the latency of 5G traffic lower than the defined threshold while also significantly reducing the number of active wavelengths. A comparison with the existing state-of-the-art techniques shows that the proposed technique results in an improvement of at least 46% and 29% in terms of the average number of active wavelengths and the average latency for 5G traffic, respectively.

Impact of Signaling System Performance on QoE in Next Generation Networks

The first experience of quality by multimedia applications’ users takes place during the setup phase of a new connection. If the setup phase is not accepted or “slowly accepted”, the confidence of the user decreases. The user becomes more sensitive when he/she pays the connections with assured quality of service (QoS). In this case, the process of call request should be also accomplished with QoS guarantees. This paper presents the signaling sub-system implemented within the EuQoS system. The EuQoS signaling process follows main assumptions of next generation networks (NGN) architecture and performs tasks related with codec agreement between multimedia end users, admission control and resource reservation functions. In this paper, we present analytical, simulation and experimental results showing the impact of signaling system performance on quality of experience (QoE) for the potential users of multi-layer EuQoS system. In particular, the presented approach aims at ensuring user QoE of the connection setup phase by ensuring QoS for transferring signaling messages by the network.

Impact of Signaling System Performance on QoE in Next Generation Networks

The first experience of quality by multimedia applications’ users takes place during the setup phase of a new connection. If the setup phase is not accepted or “slowly accepted”, the confidence of the user decreases. The user becomes more sensitive when he/she pays the connections with assured quality of service (QoS). In this case, the process of call request should be also accomplished with QoS guarantees. This paper presents the signaling sub-system implemented within the EuQoS system. The EuQoS signaling process follows main assumptions of next generation networks (NGN) architecture and performs tasks related with codec agreement between multimedia end users, admission control and resource reservation functions. In this paper, we present analytical, simulation and experimental results showing the impact of signaling system performance on quality of experience (QoE) for the potential users of multi-layer EuQoS system. In particular, the presented approach aims at ensuring user QoE of the connection setup phase by ensuring QoS for transferring signaling messages by the network.

Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

AbstractIn avionics system networking, mixed‐critical networks are commonly used to integrate system applications with different levels of guarantees for various traffic classes on a shared resource platform, effectively addressing issues such as cabling, cost, weight, volume, power consumption, etc. However, current fault recovery and reconfiguration techniques mainly focus on high‐priority time‐triggered (TT) traffic, often neglecting the impact of TT traffic reconfiguration on low‐priority traffic. This paper proposes an avionics mixed‐critical networking approach based on the IEEE 802.1CB protocol, which is shown to be more effective for the post‐failure recovery of Rate‐Constrained (RC) traffic (a typical type of low‐priority traffic in TT networks). Furthermore, an elastic reconfiguration algorithm is presented to ensure quality of service for RC traffic during and after reconfiguration, preventing arbitrary application and traffic shutdown triggered by TT traffic reconfiguration. Simulations demonstrate that the proposed approach and algorithm can significantly improve the post‐failure recovery ratio of RC traffic, as well as guarantee quality of service and network throughput of RC traffic during and after reconfiguration to a certain extent.