Quality Of Service Estimation Research Articles

AI-Enabled Framework for Mobile Network Experimentation Leveraging ChatGPT: Case Study of Channel Capacity Calculation for η-µ Fading and Co-Channel Interference

Artificial intelligence has been identified as one of the main driving forces of innovation in state-of-the-art mobile and wireless networks. It has enabled many novel usage scenarios, relying on predictive models for increasing network management efficiency. However, its adoption requires additional efforts, such as mastering the terminology, tools, and newly required steps of data importing and preparation, all of which increase the time required for experimentation. Therefore, we aimed to automate the manual steps as much as possible while reducing the overall cognitive load. In this paper, we explore the potential use of a novel Chat Generative Pre-trained Transformer (ChatGPT) conversational agent together with a model-driven approach relying on the Neo4j graph database in order to aid experimentation and analytics in the case of wireless network planning. As a case study, we present a derivation of the expression for the channel capacity (CC) metric in the case of η-µ multipath fading and η-µ co-channel interference. Moreover, the derived expression is leveraged for quality of service (QoS) estimation within the software simulation environment. ChatGPT, in synergy with a model-driven approach, is used to automate several steps: data importing, generation of graph construction, and machine learning-related Neo4j queries. According to the achieved outcomes, the proposed QoS estimation method, based on the derived CC expression (with precision up to the fifth significant digit), demonstrates satisfactory accuracy (up to 98%) and faster training than the deep neural network-based solution. On the other hand, compared to the manual approach based on our previous work, ChatGPT-based code generation reduces the time required for experimentation by more than 4 times.

Artificial Intelligence enabled Dynamic Bandwidth Allocation Optimization in IoT for improved QoS

The Internet of Things (IoT) is a collection of intelligently connected devices that are connect to the internet. Every day, as technology advances, an IoT device outnumbers humans on the world. The Internet of Things (IoT) systems would connect not only physical users and devices, but also virtual networks and devices that have already been implemented in various scenarios using new networking, computing, and integrated system technologies. With the rapid advancement of technology, the number of devices involved grows rapidly. IoT systems have crucial issues in supplying sufficient bandwidth to IoT Devices for Improving Quality in Services provided in the form of QoS (Quality of Service) parameters, depending on the volume of devices and data. The proposed work is an artificial intelligence enabled dynamic bandwidth allocation optimization in IoT for improved QoS comprising of IoT devices, router, bandwidth data, KM-PSO clustering, Deep Alternative Neural Network (DANN), Dynamic Bandwidth Allocation Optimization, and QoS Estimation. It provides a scalable solution for allocating the bandwidth for the IoT enabled devices for improving QoS parameters. The work uses K-Means-Particle Swarm Optimization (KM-PSO) for generating the optimum number of clusters for improving the accuracy of the proposed work. Once the optimal number of clusters are generated, a Deep Alternative Neural Network (DANN) used in the present invention detects the traffic from the IoT devices, predicts the required bandwidth to be allocated. The Artificial Intelligence enabled Dynamic Bandwidth Allocation Optimization (AIEDBAO) uses Fuzzy Logic Controller (FLC) for allocating Bandwidth based on the traffic rules generated. The improved QoS parameters achieved with the present invention are Bandwidth Consumption of 34.5Mbps, Packet Delivery Delay (PDD) of 32ms, Packet Loss of 4% and Throughput of 99% at supply of 50Mbps

Physical Layer Security in Downlink of Cell-Free Massive MIMO With Imperfect CSI

This paper investigates the threat of passive eavesdropping on downlink cell-free massive MIMO (CF-MaMIMO) systems, examining in a particular system under the effects of imperfect channel estimation. Physical layer security (PLS) techniques and power allocation algorithms are typically adopted to deteriorate the quality of eavesdropped signals. In the downlink stream, artificial noise (AN) is broadcasted simultaneously with the users’ data streams, with the aim of jamming the eavesdropper’s signal without sacrificing the quality of service (QoS). However, imperfect channel estimation results in AN leakage to legitimate users. Our work considers this condition, and the power allocation algorithms we present determine the minimum power that is needed to maintain the user’s Qos even in the presence of leaked AN. We first propose the cooperative PLS algorithm (COP), which facilitates AN broadcasting via access point (AP) cooperation. Unfortunately, this cooperation wastes some of the power used to send the AN and, in most cases, increases the computational complexity. We then present the independent PLS algorithm (IND), where the goal is to regain this power by allowing each AP to broadcast AN independently. However, to meet the feasibility criterion, this occurs at the cost of increasing the number of used antennas. This technique reduces the power allocation complexity but increases the computational complexities of the precoder design and the channel estimation process. Our results reveal that the proposed algorithms enhance the security performance of CF-MaMIMO. We evaluate such performance from numerous angles including the number of users, number of APs, QoS, beamforming, AN leakage, and channel estimation techniques.

QoS-aware and multi-objective virtual machine dynamic scheduling for big data centers in clouds

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

A QoS-Aware Service Composition Mechanism in the Internet of Things Using a Hidden-Markov-Model-Based Optimization Algorithm

Recently, a new technology topic has been known as the Internet of Things (IoT), where all devices like smartphones, smart TVs, medical and healthcare ones, and home appliances have been applied for data generating. Due to the variety of services, the numerous service composition problems, mostly related to the Quality-of-Service (QoS) parameters, are recognized in the IoT domain. Since this issue is an NP-hard obstacle, different metaheuristic approaches have been utilized up until now to solve it. Many varieties of services can be brought into the IoT, depending on users’ demands. In this research, we have proposed an effective way based on a hidden Markov model (HMM) and an ant colony optimization (ACO) to answer the service composition issue by enhancing the QoS. The HMM has been trained to predict QoS. The emission and transition matrices have been improved using the Viterbi algorithm. We have executed the QoS estimation using the ACO algorithm and found a suitable path. The outcomes have illustrated the efficacy of the introduced method regarding availability, response time, cost, reliability, and energy consumption compared to the previous methods.

Context-Aware and Adaptive QoS Prediction for Mobile Edge Computing Services

Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing, and has gained a considerable momentum recently. However, the dynamically changing quality of service (QoS) may result in failures of QoS-aware recommendation and composition of MEC services, which significantly degrades users’ satisfaction and negates the advantages of MEC. To address this issue, by considering user-related and service-related contextual factors and various MEC services scheduling scenarios, we propose two context-aware QoS prediction schemes for MEC services. The first scheme is designed for the situations when MEC services are scheduled in real-time, which contains two context-aware real-time QoS estimation methods. One method can estimate the real-time multi-QoS of MEC services and the other method can estimate the real-time fitted QoS of MEC services. The second scheme is designed for the situations when MEC services are scheduled in the future. This scheme includes two context-aware QoS prediction methods. One method can predict the multi-QoS of MEC services and the other method can predict the fitted QoS of MEC services. Finally, adaptive QoS prediction strategies are developed in the light of characteristics of the proposed QoS prediction methods. According to these strategies, the most appropriate QoS prediction method can be scheduled. Extensive experiments are conducted to validate our proposed approaches and to demonstrate their performance.

Modeling passengers’ perceptions of intercity train service quality for regular and special days

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in this study to model the non-linear relationship between intercity train service quality (SQ) and its attributes related to physical conditions and service features. We use Likert scale questionnaire survey data from 1037 and 553 users to calibrate the ANFIS structures for intercity train SQ estimation for regular days and special days, respectively. The influences of membership functions (MFs) and epochs on ANFIS performance are assessed to capture heterogeneity in the collected SQ data. Based on this study, it is found that the effect of epochs is insignificant for a higher number of epochs. Moreover, the Gaussian-type MF incorporated into the ANFIS structure fits the collected survey data better than other distributions. Overall, the proposed ANFIS structures with 18 attributes show 54.1% and 60.2% accuracy in predicting train SQ for regular days and special days, respectively. A stepwise approach is followed for ranking the intercity train SQ attributes influencing its overall SQ and the results are compared with those of the empirical observations (public opinions). The study implies that besides waiting place condition, attributes related to physical conditions and service features of intercity train are important determinants of its perceived SQ for regular days and special days, respectively. These results help in identifying the characteristics that are important to SQ perception. This can help transit planners and managers in targeting improvement investments that will be most effective to help commuters think more positively about their trips.

Energy and Bursty Packet Loss Tradeoff Over Fading Channels: A System-Level Model

Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience (QoE) for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross-layer design framework when channel state information (CSI) is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance tradeoff numerically and show the effect of channel uncertainty on the packet scheduler design.

QoS frameworks for Multimedia Traffic in Mobile Adhoc Networks: A Comparative Review

MANETs (Mobile Adhoc Networks) has gained an increased interest by the research community. Regular intelligent exchanges of multimedia will be typical in MANET, though the extended motivation on QoS (Quality of Service). However, various properties of the discussed QoS framework are provisioned for QoS as a challenging concern. Providing QoS provisioning is, to a great degree, challenging in MANETs in view of bouncing correspondences, center point movability and nonattendance of central coordination. Thus, most of the research has focused on giving QoS guarantees in MANETs coordinating traditions. Though huge numbers of QoS coordinating procedures have been proposed in composing, focusing on different QoS estimations yet none of the prescribed or discussed frameworks achieves a universal course of action. There exist several genuine necessary research areas of focus such as QoS metric assurance and cost limit layout, source level scheduling framework and QoS coordinating. In this paper, working and connecting of various QoS frameworks for MANETs is investigated throughout several veritable focus areas of research. After a comparative review, it is concluded that there is still a good scope of research for proposing a QoS framework for MANETs which could have cross-layer advantages, resource reservation, connection admission control, multi-constrained QoS parameters, hard QoS assurance, proactive routing advantages etc.

Spectrum Allocation and Bitrate Adjustment for Mobile Social Video Sharing: Potential Game With Online QoS Learning Approach

With the recent progress on mobile networking and devices, mobile social video sharing (MSVS) has emerged as one of the most important social media services. It enables mobile users to create ultra-short video clips and instantly share them with social friends. Due to the huge volume of videos and limited available bandwidth of wireless infrastructure, it is challenging to distribute these massive videos to mobile users with satisfactory quality of service (QoS). In this paper, we present a general framework to model the video diffusion among mobile users and user QoS of the MSVS service over the wireless infrastructure. Then, we utilize the hierarchical structure to decompose this problem into two subproblems, including a bitrate adjustment and spectrum allocation problems. For the bitrate adjustment problem, we propose a QoS estimation model based on the large deviation principle. By introducing a sliding window method to derive the online estimation, we develop an online bitrate adjustment strategy without relying on any prior knowledge of neither network environment nor video traffic. For the spectrum allocation problem, we prove that such a problem is a potential game. We devise a decentralized algorithm to find the Nash equilibrium, and analyze the convergence rate and the performance gap with the centralized optimization solution. Through extensive real trace driven simulations, we demonstrate that our proposed algorithm can guarantee smooth video playback with a higher PSNR.

An Estimation of QoS for Classified Based Approach and Nonclassified Based Approach of Wireless Agriculture Monitoring Network Using a Network Model

Wireless Sensor Network (WSN) can facilitate the process of monitoring the crops through agriculture monitoring network. However, it is challenging to implement the agriculture monitoring network in large scale and large distributed area. Typically, a large and dense network as a form of multihop network is used to establish communication between source and destination. This network continuously monitors the crops without sensitivity classification that can lead to message collision and packets drop. Retransmissions of drop messages can increase the energy consumption and delay. Therefore, to ensure a high quality of service (QoS), we propose an agriculture monitoring network that monitors the crops based on their sensitivity conditions wherein the crops with higher sensitivity are monitored constantly, while less sensitive crops are monitored occasionally. This approach selects a set of nodes rather than utilizing all the nodes in the network which reduces the power consumption in each node and network delay. The QoS of the proposed classified based approach is compared with the nonclassified approach in two scenarios; the backoff periods are changed in the first scenario while the numbers of nodes are changed in the second scenario. The simulation results demonstrate that the proposed approach outperforms the nonclassified approach on different test scenarios.

A practical model for ensemble estimation of QoS and QoE in VoIP services via fuzzy inference systems and fuzzy evidence theory

Nowadays, there is an increasing number of Voices over IP (VoIP) services offered in telecommunication networks with specific quality requirements. Such requirements impact upon both the objective quality of service (QoS) of the end-to end connection as well as the subjective quality of experience (QoE) as perceived by the end user. In this study, we have proposed an integrated QoS and QoE evaluation system based on the combination of fuzzy inference systems and fuzzy evidence theory. For quality criteria, we have used QoS as a technical source from the viewpoint of service provider and QoE as the end user approach in front of service. We have divided the positive and negative variables of QoE into two distinct systems. At the same time, a parameter has been proposed as quality, which is the total quality of a service according to the objective and subjective viewpoints. To calculate the parameters mentioned above, a fuzzy inference system has been utilized. In addition, to obtain all data sources as an evidence of quality, Dempster---Shafer evidence theory has been employed. Finally, the proposed approach evaluated through the quality of VoIP services in three real cases and the results are discussed.

AN APPROACH TO WEB SERVICE SELECTION BASED ON COMPOSITE QOS PARAMETERS

Quality of service (QoS) is an important attribute for selecting a service during the service composition process. Although availability and reliability have been considered as the predominant factors for estimating reputation, two aspects are missing in the literature. First, their use is limited to composite service level and does not count on the atomic level. Second, their combined effect is not evaluated. Better estimation of QoS can be done with both factors considered together, whereas when taken separately for computing reputation, availability will give the probability of a service being up/running and reliability will analyze the change in failure trend of that service. In this research, a mathematical modeling of predominant QoS factors, availability and reliability of atomic services using Markov Chain model and Weibull analysis respectively, are suggested. Also importance of modeling reputation as an aggregation of availability and reliability has been explained. This research concludes two results. First, counting on probability of a service being up/running and its failure trend, together, results in a better estimation of its behavior and helps selecting the most appropriate one. Second, this resulted in selection of a service with higher reputation but lower usage cost, as opposed to using a single factor that resulted in higher reputation with higher cost.

Applied Technology in the Grid Workflow Quality of Service Calculation and Estimation

For the grid workflow system include a large of candidate services, the activity scheduling in workflow’s ex-ecution base on quality of service (QoS) constraints can pro-vide optimal service for users. This paper established the QoS’s parameter system with applied technology of grid workflow, and introduced the each parameter’s calculation model of activity’s QoS, the grid work-flow’s QoS estimation method based on the basic flow control structures defined by the abstract grid workflow language (AGWL). The experimental results show that the calculation method in this paper can estimate stably QoS parameter values of grid workflow combining with the multi candidate services, and, can provide a reasonable basis for process scheduling based on QoS constraints.

A grid workflow Quality-of-Service estimation based on resource availability prediction

Accurate estimation of workflow Quality of Service (QoS) enhances the efficiency of scheduling algorithms. The availability and performance variations of Grid computing resources have made this estimation a great challenge. Most workflow QoS estimation algorithms are based on static performance of resources. In this paper, based on resources availability prediction, we propose an algorithm called WQE for estimating the QoS of a Grid workflow. WQE consists of two phases: resource monitoring and analysis and workflow QoS computation. In the first phase, two prediction algorithms are proposed to stochastically predict the availability state of resources. In the second phase, the QoS of each activity is estimated based on the host availability prediction result. The QoS of basic structures is computed by aggregating the QoS of their operands. Using a tree structure corresponding to the workflow, the QoS of basic structures is used to compute the total QoS of the workflow. The simulation results on Notre Dame University trace showed that the proposed method has higher estimation accuracy in comparison with HEFT.

GLR test for OFDM system identification using pilot tones pattern

In the context of cognitive network architecture, an opportunistic cognitive receiver must identify the present active networks. In this article, we propose an efficient algorithm for the identification of OFDM networks exploiting the pilot patterns used in these standards which are prescribed uniquely by their underlying standards. These pilots are inserted for the channel estimation and synchronization between the base stations and their users. The proposed generalized likelihood ratio test (GLRT) not only allows a cognitive observer to detect the active networks by analyzing the observed signals but also performs channel estimation, time-frequency synchronization as well as estimation of the noise variance. These informations are of a great interest for Quality of Service estimation in the purpose of an association with the base station. The proposed solution is applicable to the existing standards (e.g., LTE, WiMAX, WiFi), doesn’t require any signaling overhead to be embedded on the pilot tones, is computationally inexpensive and only requires to know the pilot patterns. An other GLRT is proposed as a pre-detector which ignores the pilot information and allows to reduce the computational cost of the system for the cases where a large number of patterns/systems are to be tested.

An Approach to Composite QoS Parameter based Web Service Selection

In any enterprise application Quality of Service (QoS) is an important attribute for selecting a service during the service composition process. Although availability and reliability have been considered as the predominant factors for estimating reputation, two aspects are lacking in the literature. First, their use is limited to composite service level and does not count at the atomic level. Second, their combined effect is not evaluated. Hence, the methodology of estimating reputation and its use for atomic service will have compounding effect on the overall quality of the composite service. We feel, better estimation of QoS can be done with both factors considered together on the simple premise that availability tells about only the probability of that service being up/running, but doesnot tell about its failure trend. In our work we present mathematical modelling of these predominant QoS factors using Markov model and Weibull analysis. A scenario has been simulated using Colored Petri Nets (CPN) to study the behavioral aspects. The outcome of our research is two fold. First, counting on probability of a service being up/running and its failure trend, together, results in a better estimation of its behavior and helps selecting the most appropriate one. Second, this resulted in selection of a service with higher reputation but lower usage cost, as opposed to using a single factor that resulted in higher reputation with higher cost.

A heuristic adaptive QoS prediction scheme in single-hop passive star coupled WDM optical networks

In this paper, a novel quality of service (QoS) prediction framework is proposed to accommodate different applications with various QoS requirements. The purpose of the proposed structure is to estimate, given the QoS requirements of different applications, the number of them can be satisfied according to the predefined QoS level and the number of them have to be rejected or accepted under lower level. Moreover, this structure is able to control the network behavior when the new application with certain QoS requirements requesting for the connection. The framework consists of four modules. QoS Requirements Monitoring detects the changes in the supplied QoS. QoS Prediction performs at real-time to estimate whether the new QoS can be adapted to the current QoS. QoS Violation is dedicated to detect the violation. And a module of QoS Estimation is used to make the decision whether the hopeless new traffic is refused or accepted with acceptable QoS requirements. Therefore, this structure is able to control network behavior when the surrounding environment changes. Numerical and simulation results obtained suggest that the proposed predictive scheme is a promising approach for real time QoS prediction.

Denial of Service Prevention for 5G

5G wireless mobile communication is expected to include a large number of advanced technologies in order to further increase bandwidth, Quality of Service (QoS), improve usability and security, decrease delays and cost of service. Software Defined Radio (SDR) will be the platform for advanced terminals. Our focus is security and especially prevention of Denial of Service (DoS) attacks which we believe will become more common in commercial networks through increasing availability of easy programmable SDRs. We propose a secret version of Adaptive Frequency Hopping, as a possible 5G technology in combination with others, with QoS estimation by means of physical layer parameters. The QoS parameter chosen is Bit Error Rate (BER) and this we use for blacklisting frequencies that we assume are under DoS attack. Our experimental results on BER estimation show this to be a promising approach.

Towards Web Service selection based on QoS estimation

It is very important to select required Web Services (WSs) according to Quality of Service (QoS) for service customers in web-based composition systems. In this paper, we propose a WS Selection Approach based on QoS Estimation (WSSAQE). The aim of WSSAQE is to perform accurate QoS estimation, and then alleviate the deviations between requiring and receiving QoS in WS selection. Experimental results show that our proposed WSSAQE is effective and efficient. Moreover, it significantly improves the QoS-based WS selection process.