Quality Of Service Criteria Research Articles

Quality of Service Based on Scheduling in Cloud/Fog Environment

In the area of fog and cloud computing, efficient work scheduling is critical for optimising resource allocation and raising Quality of Service (QOS) levels. Research in this area has resulted in a variety of ways for dealing with this problem. One line of research focuses on lowering data latency and improving QOS in fog-cloud systems by precise job scheduling. This requires intelligently dividing jobs across fog and cloud resources to reduce data processing and delivery delays, ultimately improving the overall user experience. Furthermore, the investigation encompasses novel methodologies such as hybrid evolutionary algorithms and heuristic strategies. These approaches seek to create a compromise between competing objectives, such as cost and energy efficiency, while also achieving QOS criteria. Researchers aim to develop scheduling systems that optimise resource utilisation and reduce operating costs without sacrificing service quality by applying evolutionary concepts or combining heuristic methods. Furthermore, ongoing discussions focus on improving resource scheduling approaches to achieve optimal QOS results. This entails continual evaluation and adaption of scheduling algorithms to changing environmental conditions, workload fluctuations, and user requests. Furthermore, developing methodologies such as the CODA approach provide intriguing avenues for efficient job scheduling in fog computing settings, with the potential to revolutionise future resource allocation and QOS enhancement efforts.

An effective deep-Q learning scheme for QoS improvement in physical layer of software-defined networks

The demands of diverse applications necessitate specific communication requirements, such as jitter, packet loss ratio (PLR), and latency, at the end-to-end (E2E) level within the physical layer of software-defined networking (SDN). In heterogeneous network environments, E2E routes traverse multiple domains with varying quality-of-service (QoS) classes of traffic, posing challenges in meeting unique E2E QoS criteria. Hence the performance on the E2E path is significant to meet the needs of applications. Hence, the issue of providing services on the E2E route is challenging due to several QoS classes in a domain on the source-to-destination route. This paper presents an SDN architecture leveraging Deep Q-learning (DQL) to ascertain the appropriate QoS class for E2E routes in software-defined heterogeneous networks, thereby reducing overall E2E delay. The proposed model with DQL is compared with benchmark reinforcement learning. The proposed framework is validated using various real Internet topologies such as Abilene, USNet, and OS3E, assessing not only E2E delay but also jitter, PLR, and throughput, showcasing its efficacy in optimizing network performance regarding these performance metrics.

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.

Optimization of transport constraints and quality of service for joint resolution of uncertain scheduling and the job-shop problem with routing (JSSPR) as opposed to the job-shop problem with transport (JSSPT)

To better meet the qualitative and quantitative requirements of customers or relevant sector managers, workshop environments are implementing increasingly complex task management systems. The job shop scheduling problem (JSSP) involves assigning each task to a single machine while scheduling many tasks on different machines. Finding the best scheduling for machines is one of the challenging optimizations of difficult non-deterministic polynomial (NP) time problems. The fundamental goal of optimization is to shorten the makespan (total execution time of all tasks). This paper is interested in the joint resolution of scheduling and transport problems and more particularly the Job-shop problem with Routing (JSSPR) as opposed to the Job-shop problem with Transport (JSSPT). These two problems are modeled in the form of a disjunctive graph. For the JSSPT, the solution to the transport problem is not linked to any quality of service (QoS) criterion and the solution is therefore often semi-active. The Job-shop with Routing explicitly considers transport operations and uses algorithms from the transport community to solve the transport problem. It is shown that the routing part of the JSSPR is a problem of the vehicle routing family and of the Pickup and Delivery Problem family. QoS in the JSSPR is defined by the duration of tours, the duration of transport of parts and the waiting time for them. A new evaluation function – named Time-Lag Insertion Heuristic (TLH) – is proposed to evaluate a disjunctive graph by simultaneously minimizing the makespan and maximizing the quality of service. Thus, the solution obtained is not semi-active, but a compromise between the different criteria. This evaluation function is included in a metaheuristic. Our numerical evaluations demonstrate that, on the one hand, the TLH evaluation can find almost optimal solutions regarding the QoS criterion; and on the other hand, the TLH evaluation is not very sensitive to the order of insertion of the maximum time-lags during the different minimization steps.

Improved QoS with Fog computing based on Adaptive Load Balancing Algorithm

As the number of sensing devices rises, traffic on the cloud servers is boosting day by day. When a device connected to the IoTwants access to data, cloud computing encourages the pairing of fog & cloud nodes to provide that information. One of the key needs in a fog-based cloud system, is efficient job scheduling to decrease the data delay and improve the QoS (Quality of Service). The researchers have used a variety of strategies to maintain the QoS criteria. However, because of the increased service delay caused by the busty traffic, job scheduling is impacted which leads to the unbalanced load on the fog environment. The proposed work uses a novel model which curates the features and working style of Genetic algorithm and the optimization algorithm with the load balancing scheduling on the fog nodes. The performance of the proposed hybrid model is contrasted with the other well-known algorithms in contrast to the fundamental benchmark optimization test functions. The proposed work displays better results in sustaining the task scheduling process when compared to the existing algorithms, which include Round Robin (RR) method, Hybrid RR, Hybrid Threshold based and Hybrid Predictive Based models, which ensures the efficacy of the proposed load balancing model to improve the quality of service in fog environment.

An MCDM optimization based dynamic workflow scheduling used to handle priority tasks for fault tolerance in IIOT

Cloud computing's prominence can be mainly attributed to its convenient on-demand service delivery via the internet. The difficulty in managing these requests and allocating them optimally is exacerbated by the growing number of users making them and the variety of criteria used to apply cloud resources. Some of these difficulties include scheduling work while considering the user's Quality of Service (QoS) preferences, ensuring adequate load balancing, and adapting to the ever-changing conditions of the cloud environment. As server capabilities evolve, static load balancers, which distribute requests using fixed ratios of available resources, fail to provide optimum throughput. It has been shown that no other scheduling algorithms offer an adaptive method that considers both load balancing and maximizing the QoS criteria. The authors of this study present a novel adaptive strategy for dealing with this issue by fusing the best-worst multi-criteria decision-making method (BWM) with the compromise ranking method (VIKOR). Task priorities are determined by using the VIKOR method as a decision-maker. Numerical experiments are used to verify the validity of the suggested method, and the methodology is compared to other scheduling methods in terms of a variety of performance criteria. The simulation findings show that the presented method outperforms its rivals regarding throughput, makespan, waiting time, virtual machine (VM) utilization, and VM usage cost across the board of simulated experimental settings.

SNR-Outage-Based Robust Artificial Noise-Aided Beamforming for Correlated MISO Wiretap Channels Under Gaussian Channel Uncertainties

This article studies robust artificial noise-aided beamforming for correlated multiple-input-single-output eavesdropping channels under Gaussian channel uncertainties. In the case of high reception correlation, under a secrecy outage probability constraint, the achievable secrecy rate is very small and does not satisfy the requirements of some high-rate applications. In view of this, optimization designs on the premise of quality-of-service (QoS) assurance are considered. By exploiting the correlation and employing a novel QoS criterion of the signal-to-noise ratio (SNR) outage probability, two optimization problems, namely minimization of the target SNR at eavesdroppers and maximization of the loose secrecy rate, are formulated. Despite less rigorous secrecy, a much lower target SNR at eavesdroppers and a higher loose secrecy rate are achieved. Compared with the conventional SNR-QoS, SNR-outage-QoS is a more flexible criterion that can be applied to Gaussian channel uncertainties, although challenging probabilistic constraints are introduced into the optimization problems. To make the optimization problems feasible, two approaches, namely, the Bernstein-type inequality approach and ellipsoid bounding approach, are applied to approximate the probabilistic constraints. Simulation results show that the robust transmit designs contribute to a secrecy performance gain from the perspective of SNR-outage-QoS.

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

SummaryRecently, with the rapid development of the Internet of Things (IoT) and its real‐time applications, the network is required to guarantee the differential quality of service (QoS) requirements of the various data flows (telemedicine, video, and live streaming) of various IoT services. Software‐defined network (SDN) as emerging network technology separates the control logic from data planes of networks and is a promising technique to guarantee the QoS requirements of different services. In SDN, the controller has global visibility of the whole network devices such as switches and routers. Hence, controllers can dynamically optimize QoS requirements and resources of the network. On the other hand, providing a QoS‐aware routing scheme is an important challenge. Hence, in this paper by capitalizing on the fuzzy analytic hierarchy process (AHP), a new QoS‐aware routing method is proposed in SDNs. The proposed scheme considers the combination of different QoS criteria such as bandwidth, route length, reliability, and bit error rate for selecting an appropriate route between sender and receiver nodes. The simulation results indicated that the proposed method improves throughput and end‐to‐end delay. Comparison with other routing algorithms shows that the proposed method performs better load balancing under different network topologies.

Packet Loss Optimization in Router Forwarding Tasks Based on the Particle Swarm Algorithm

Software-defined networks (SDNs) are computer networks where parameters and devices are configured by software. Recently, artificial intelligence aspects have been used for SDN programs for various applications, including packet classification and forwarding according to the quality of service (QoS) requirements. The main problem is that when packets from different applications pass through computer networks, they have different QoS criteria. To meet the requirements of packets, routers classify these packets, add them to multiple weighting queue systems, and forward them according to their priorities. Multiple queue systems in routers usually use a class-based weighted round-robin (CBWRR) scheduling algorithm with pre-configured fixed weights for each priority queue. The problem is that the intensity of traffic in general and of each packet class occasionally changes. Therefore, in this work, we suggest using the particle swarm optimization algorithm to find the optimal weights for the weighted fair round-robin algorithm (WFRR) by considering the variable densities of the traffic. This work presents a framework to simulate router operations by determining the weights and schedule packets and forwarding them. The proposed algorithm to optimize the weights is compared with the conventional WFRR algorithm, and the results show that the particle swarm optimization for the weighted round-robin algorithm is more efficient than WFRR, especially in high-intensity traffic. Moreover, the average packet-loss ratio does not exceed 7%, and the proposed algorithms are better than the conventional CBWRR algorithm and the related work results.

Q2A-NOMA: A Q-Learning-Based QoS-Aware NOMA System Design for Diverse Data Rate Requirements

Wireless use cases in the industrial Internet of Things networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although nonorthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong device interference. In this article, we propose a Q-learning-based algorithm for handling many-to-many matching problems, such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection [orthogonal multiple access or NOMA], and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.

Driver’s Perceived Satisfaction at Urban Roundabouts—A Structural Equation-Modeling Approach

In recent years, the use of roundabouts in road networks has reflected a sustainable and modern solution for traffic intersections. Their implementation, integrated design, and proper evaluation are a necessity to achieve their beneficial results. According to the latest edition of the Highway Capacity Manual (HCM) and the Highway Capacity and Quality Service (HCQS), a comprehensive evaluation of a roundabout is based on the interchangeable use of the LOS (level of service) and QOS (quality of service). Both manuals describe the LOS criteria, which are the same as those currently used for unsignalized at-grade intersections, while the QOS methodological description and criteria are not specifically defined. The quality of service, which corresponds to road users’ perceived satisfaction, is determined by identifying and evaluating certain factors that have an impact on users. While the previous work on evaluating the quality of roundabouts is limited, this work aimed to present and evaluate the concept of QOS for urban roundabouts in Greece and to assess the factors that affect drivers’ perceived feeling of comfort. The methodology used for the research, included data collection via an on-line questionnaire addressed to the users of the Greek road network and a statistical analysis based on the performance of six latent variables named quality components (exploratory factor analysis). A structural equation model (SEM) was used to determine the causal relationship between primary factors and quality components. It was noted that the SEM cannot predict the travel behavior but it has ability to express relationships between unobserved and observed variables. The results of the revealed model are of great value for the development of: (a) a comprehensive conceptual framework of the QOS and (b) a critical analysis of the parameters that should be considered for the assessment of the QOS of a roundabout. Identifying the factors that influence road users’ perception in terms of safety and comfort (quality of service) leads to a better knowledge and understanding of the road network characteristics that are important to road users and that influence their behavior and level of satisfaction.

Study of a linear-physical-programming-based approach for web service selection under uncertain service quality

Abstract To promote the practical use of web service technologies, it is critical to select a proper web service that will meet users’ preferences from a pool of web services with similar functions. It is usually a core issue during web service selection to describe and evaluate the quality of these web services. Average value can be employed to describe certain service quality and to evaluate the quality with multiple criteria through the Simple Additive Weight Method. Based on an analysis of various deficiencies in existing approaches proposed in some studies, four indicators were used: Price, Response Time, Reliability and Credibility. On top of that, recent sampling data sets were introduced to describe the uncertain quality of service (QoS) criteria; linear physical programming was used to evaluate QoS with comprehensive criteria to meet the need to objectively describe uncertain QoS criteria, and preference regions were set to adapt to interval characteristics of criteria. Finally, a computational example is provided to demonstrate the feasibility and effectiveness of the method.

Quality of experience aware network selection model for service provisioning in heterogeneous network

Heterogeneous wireless networks (HWNs) are capable of integrating the different radio access technologies that make it possible to connect mobile users based on the performance parameters. Further quality of service (QoS) is one of the major topics for HWNs, moreover existing radio access technology (RAT) methodology are designed to provide network QoS criteria. However, limited work has been carried out for the RAT selection mechanism considering user QoS preference and existing models are developed based on the multi-mode terminal under a given minimal density network. For overcoming research issues this paper present quality of experience (QoE) RAT (QOE-RAT) selection methodology, incorporating both network performance criteria and user preference considering multiple call and multi-mode HWNs environment. First, this paper presents fuzzy preference aware weight (FPAW) and multi-mode terminal preference aware TOPSIS (MMTPA-TOPSIS) for choosing the best RAT for gaining multi-services. Experiment outcomes show the QOE-RAT selection method achieves much superior packet transmission outcomes when compared with state-of-art Rat selection methodologies.

Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.

Multicriteria evaluation of the quality of service of informal public transport: An empirical evidence from Ibadan, Nigeria

Developing countries experience a decline in bus public transport investments. Yet informal public transport has continued to grow rapidly. Previous studies have evaluated the Quality of Service (QoS) provided by such informal transport but a multi-criteria evaluation that considers various stakeholders in the Global South is missing. A case study was carried out in three local government areas in Ibadan, the third largest city in Nigeria, with a focus on identifying criteria to develop an evaluation model for QoS. Information on user perceptions of the identified QoS criteria was collected through the administration of questionnaires onboard and offboard, using stratified system random sampling. Weights of the variables for the determination of the QoS of the IPT were determined from stakeholder focus groups. Weights were assigned to each of the observed variables of the set criteria, which serve as indicators for the assessment of the quality of service. The multi-criteria included accessibility, affordability travel, waiting time, travel time, seat comfort, transport fares, safety, and the drivers’ attitude. Findings from the application of the multi criteria evaluation model indicate an all-round low quality of service to all criteria applied. In particular, the study observes a low accessibility of informal transport service in Ibadan compared to other similar studies. The poor service quality experienced make the transport cost considerably unaffordable (value for money) given low quality in-bus comfort due to poor seats and crowding at terminals and in bus, low safety standards due to road unworthy buses, long travel time due to delays at loading terminals and undesignated frequent in passenger pick and drop points, and experiences of aggressive and reckless drivers. This paper advances new understanding in two arenas: firstly, that informal public transport has positive contribution to the Nigerian economy in general but needs policy support for strengthening its resilience; secondly, a new methodology, the multi-criteria evaluation model that was developed in this paper has potential for transferability in the evaluation of the quality of service of IPT. This is because the model although showed that QoS of IPT in the case study area of Ibadan is low however has demonstrated its ability to integrate range of criteria that are context-specific, for assessing the QoS of public transport.

Service Quality Evaluation of Terminal Express Delivery Based on an Integrated SERVQUAL-AHP-TOPSIS Approach

With the rapid development of e-commerce and information technology, the express package volume in the Chinese market keeps an explosively increasing tendency. Terminal delivery plays a significant role on the business and reputation promotion of express brand. To identify the service quality of delivery service, an integrated SERVUQAL-AHP-TOPSIS approach is developed to evaluate the quality of service (QoS) of the city express industry. Firstly, the QoS criteria system is established through SERVQUAL-based dimensions. Secondly, the AHP method is employed to derive the relative weights of criteria. Then, the two stages are embedded into the TOPSIS steps to evaluate the service quality of the express alternative. An application case study is conducted following the detailed steps of the proposed integrated decision-making framework, and results demonstrate the effectiveness and validity of the proposed approach on the QoS of terminal delivery evaluation problem.

Link Scheduling Algorithm to Mitigate Worst-Case Interference Problems for LTE-A

As the number of apps increases, the traffic of multimedia services in wireless networks grows exponentially. Because of the proportional fair algorithm's shortcomings in terms of achieving Quality of Service (QoS) requirements such as delay and packet loss, it is not extensively employed in modern wireless communication. This study provides a scheduling method based on the QoS utility function, with a parameter added to improve the priority mechanism in order to overcome such flaws. Our scheduling algorithm can match the QoS criteria of real-time traffic, according to simulation results, and the overall system transmission performance has increased significantly.

Optimizing Quality of Service of Clustering Protocols in Large-Scale Wireless Sensor Networks with Mobile Data Collector and Machine Learning

The rise of large-scale wireless sensor networks (LSWSNs), containing thousands of sensor nodes (SNs) that spread over large geographic areas, necessitates new Quality of Service (QoS) efficient data collection techniques. Data collection and transmission in LSWSNs are considered the most challenging issues. This study presents a new hybrid protocol called MDC-K that is a combination of the K-means machine learning clustering algorithm and mobile data collector (MDC) to improve the QoS criteria of clustering protocols for LSWSNs. It is based on a new routing model using the clustering approach for LSWSNs. These protocols have the capability to adopt methods that are appropriate for clustering and routing with the best value of QoS criteria. Specifically, the proposed protocol called MDC-K uses machine learning K-means clustering algorithm to reduce energy consumption in cluster head (CH) election phase and to improve the election of CH. In addition, a mobile data collector (MDC) is used as an intermediate between the CH and the base station (BS) to further enhance the QoS criteria of WSN, to minimize time delays during data collection, and to improve the transmission phase of clustering protocol. The obtained simulation results demonstrate that MDC-K improves the energy consumption and QoS metrics compared to LEACH, LEACH-K, MDC maximum residual energy leach, and TEEN protocols.

Time-space analysis to evaluate cell-based quality of service in bus rapid transit station platforms through passenger-specific area.

It is important to evaluate the quality of service (QoS) of bus rapid transit (BRT) station platform operation. Passenger-specific area (PSA) is used as a QoS measure which is determined by considering passenger activities separately. As passengers perform various activities on the same platform space, there is a need to evaluate BRT platform QoS by considering the activities collectively. When evaluating transit station platforms, many researchers calculated PSA for the whole platform area, while very few researchers highlighted the importance of evaluating the platform as small, partitioned areas. By considering these findings and gaps in the literature, this study evaluates QoS of the platform on a cell by cell basis using PSA. We use time–space analysis and passenger-minutes of each activity to develop a methodology to determine PSA, by considering stationary passengers, circulating passengers, and passengers overall. To evaluate platform QoS, we define threshold service levels using passenger-minutes of activities and Fruin’s QoS criteria. For the case study BRT station, we find that PSA varies significantly between platform cells. It is evident from the results that it is important to identify highly congested areas in the platform and apply measures to improve platform QoS.

Clustering Methods Survey in VANET on Roads and Mountain Trails with High Reliability

VANETs are one of the important technologies in recent decade. This importance is evident which can be used in road and mountains trails and also provides other information to vehicle from roads. There are many challenges and problems in this networks which can cited as routing, clustering, quality of services criteria, energy consumption, RSU placement and etc. This article try to study and survey about VANET’s clustering methods in roads and mountain trails with high reliability.