Quality Of Service In Networks Research Articles

Optimization of Quality of Service in 5G Cellular Network by Focusing on Interference Management

Optimization of Quality of Service in 5G Cellular Network by Focusing on Interference Management

Dynamic event-triggered control for CPSs under QoS-based variable sampling approach

In this article, a quality of service (QoS) dependent variable sampling dynamic event-triggered control method is designed for a cyber–physical system (CPS) with delays and packets dropout to cope with non-ideal network environments, maintain the desired control performance and improve the communication efficiency. To achieve the variable period sampling, a sampler is designed based on the QoS of the wireless network by using the delta operator discretization method. Then, a variable period sampling scheme for the delta operator system converted from the CPS is designed. Furthermore, a dynamic event-triggered mechanism (DETM) is proposed using the variable period sampling signal, which can reduce event triggered data calculations and increase event triggered intervals. By utilizing the average dwell time (ADT) approach, sufficient conditions contains the explicit variable sampling period are derived for the derived switched CPS. Finally, the effectiveness of the designed method is verified by numerical examples.

A lightweight BPSO mechanism for topology reconfiguration in data-driven IIoT plants

Wireless Sensor Networks in large-area monitoring applications may be required to deal with emergency events, i.e., events from regions that suddenly require close attention. Nodes in these regions may demand increased data generation rates, and their communication with the base station (BS) should be prioritized. In this sense, a significant challenge is how to reconfigure the network to create paths that offer adequate Quality of Service (QoS) between these sensor nodes and the BSs. Depending on the network’s size, the computational complexity of this problem may become unacceptable; but a meta-heuristic approach could handle it. This work describes a lightweight Binary Particle Swarm Optimization (BPSO) mechanism for the topology reconfiguration of data-driven cluster-tree networks based on IEEE 802.15.4 standard. The goal of this mechanism, called CALiPSO, is to reduce the computational complexity when searching for a suitable network topological configuration that improves network QoS metrics. A simulation assessment and comparison with other approaches were conducted. It was demonstrated that CALiPSO can reconfigure a network of 125 nodes and create an appropriate cluster-tree with fewer cluster-heads in hotspot regions, while improving network QoS. Compared with heuristic and optimization methods, CALiPSO selects around 42% to 55% fewer CHs and forms trees 22% less depth. Concurrently, it diminishes communication delays by about 5% and decreases packet losses by 7.5%, without jeopardizing energy consumption.

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.

Analysis of Wireless Multimedia Big Data Transmission Based on Adaptive Scheduling Algorithm

The increasing demand for wireless multimedia applications has prompted the need for efficient data transmission strategies that can adapt to dynamic network conditions and diverse Quality of Service (QoS) requirements. This paper analyzes wireless multimedia big data transmission using an innovative Adaptive QoS-Aware Scheduling Algorithm (AQOS-ASA). The proposed algorithm aims to optimize the transmission of multimedia data by dynamically adjusting the scheduling based on QoS considerations and real-time network conditions. Through adaptive scheduling, the algorithm ensures that high-priority multimedia data receives optimal transmission treatment, balancing the quality preferences set by users. The algorithm dynamically allocates time slots based on the changing network conditions, allowing for efficient utilization of the available bandwidth. The paper analyses the algorithm's performance in different wireless network scenarios through simulations and evaluations, highlighting its effectiveness in adapting to varying QoS requirements and network conditions. The results demonstrate that the proposed algorithm significantly enhances the quality and efficiency of wireless multimedia big data transmission, making it well-suited for applications with diverse multimedia content and dynamic network environments. The findings contribute valuable insights to wireless multimedia communication and provide a foundation for further advancements in adaptive scheduling algorithms for improved Quality of Service.

Sketching of interactive VoIP traffic with multivariate statistical learning-based classification

Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today’s network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system’s high accuracy, consistently around 98-99%. These findings affirm the system’s suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management.

A Queue Monitoring System in OpenFlow Software Defined Networks

Real-time trac characteristic is dierent and it is very sensitive to delay. To meet trac specications in real time, monitoring systems are used as an important part of networking. Many monitoring systems are deployed to have an update view of the network QoS parameters and performance. Most of these systems are implemented to measure QoS parameters in links. Here, in this paper, a system for monitoring queues in each link by means of Software Defined Networks is proposed. The monitoring system is implemented by extending Floodlight controller, which uses OpenFlow as southbound protocol. The controller has a centralized view of the network. By the help of OpenFlow it also can provide flow level statistics. Using these advantages, the proposed system can monitor delay and available bandwidth of a queue on a link or path. Despite of monitoring systems in traditional networks, the proposed monitoring system makes a low overhead in network thanks to OpenFlow protocol messages. It is also integrated into the network controller, which enables QoS and trac engineering applications to use the system's reports for automatic trac management and QoS setup. The experimental results show a 99% accuracy of the proposed system for monitoring of both bandwidth and delay.

“Hybrid MAC Methodology for Improving the QOS in Fiber Wireless Network”

The Fi-Wi (Fiber-Wireless) approach stands out as a crucial element in the realm of networks, demonstrating superiority over various technologies. With the exponential growth in Internet users, significant strides have been made in the evolution of Fi-Wi networking systems in recent years. This mechanism offers broader bandwidth and network stability, ensuring high-speed connectivity with "Anytime Anywhere" availability for end users. However, the escalating energy demand in networking systems poses a constraint on the network's lifespan, impacting transmission.
 Over the years, researchers have proposed and tested various Media Access Control (MAC) protocols to address transmission and energy consumption issues. Despite these efforts, existing protocols have encountered challenges such as overheating, delays, throughput issues, and collisions. This research paper introduces a combination of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) to tackle associated challenges. The primary objective is to enhance throughput and reduce delays in Fi-Wi networks.
 To achieve this goal, the study employs techniques that involve and Utilizing an Orthogonal Frequency Division Multiplexing (OFDM) modulator, a free-space optical (FSO) communication channel, an OFDM demodulator, and Opti-system for the analysis and enhancement of received signals, our study demonstrates that the proposed MAC protocol surpasses conventional MAC protocols in terms of delay, data throughput, and transmission efficiency.

Investigation Performance Analysis and Evaluation of VANET Routing Protocol on Urban Scenario Simulation: A Case Study of Melaka

Vehicles on the road create a VANET network using IEEE 802.11P for wireless data exchange. However, VANET is challenging since the characteristics of VANET such as high mobility and frequent topology making it harder to design an effective routing protocol. The needs of an efficient performance routing protocol are crucial since it is to ensure a better network Quality of Service (QoS). In this paper, the evaluation of the IEEE 802.11P performance is simulated in real-world scenario using the Melaka City roadmap. In an urban setting, the impact of vehicle density on the performance of VANET routing protocols including OLSR, AODV, and DSDV are examined. Reviewing the effectiveness of the current VANET routing protocols is the goal of this research since it is crucial to know which routing has high performance under specific circumstances. Quantitative measurements including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), and end-to-end delay are evaluated using the Network Simulator NS-3 and SUMO. Findings from the investigation show that while proactive routing protocols like OLSR and DSDV perform better in terms of end-to-end delay, AODV performs exceptionally well in terms of PDR and PLR.

A Task Offloading Method Based on User Satisfaction in C-RAN With Mobile Edge Computing

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this paper proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This paper proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

Penerapan Metode Quality of Service (QoS) dalam Analisis Kualitas Jaringan Internet Burjo Pantry Semarang

The rapid progression of technology enables humans to exchange data and communicate remotely, eliminating the need for face-to-face interactions. The internet network, a product of this technological advancement, undergoes evaluation through the Quality of Service (QoS) method, which measures network service quality. This research focuses on assessing the internet network quality at Burjo Pantry in Semarang, utilizing a quantitative approach with a Quality of Service analysis. The throughput results reveal 1682Kbps in the terrace area and 7266Kbps indoors at Burjo, both categorized as 'very good.' Likewise, the delay, with 128,75ms in the terrace and 145ms indoors, falls into the 'very good' category. Both areas exhibit excellent jitter: 1,77ms for the terrace and 26,46ms indoors. Packet loss is minimal, with 1,73% in the terrace area and 0,68% indoors, both considered 'very good.' Overall, the study indicates the commendable quality of the internet network at Burjo Pantry in Semarang.

“Hybrid MAC Methodology for Improving the Qos in Fiber Wireless Network”

The Fi-Wi (Fiber-Wireless) approach stands out as a crucial element in the realm of networks, demonstrating superiority over various technologies. With the exponential growth in Internet users, significant strides have been made in the evolution of Fi-Wi networking systems in recent years. This mechanism offers broader bandwidth and network stability, ensuring high-speed connectivity with "Anytime Anywhere" availability for end users. However, the escalating energy demand in networking systems poses a constraint on the network's lifespan, impacting transmission.
 Over the years, researchers have proposed and tested various Media Access Control (MAC) protocols to address transmission and energy consumption issues. Despite these efforts, existing protocols have encountered challenges such as overheating, delays, throughput issues, and collisions. This research paper introduces a combination of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) to tackle associated challenges. The primary objective is to enhance throughput and reduce delays in Fi-Wi networks.
 To achieve this goal, the study employs techniques that involve and Utilizing an Orthogonal Frequency Division Multiplexing (OFDM) modulator, a free-space optical (FSO) communication channel, an OFDM demodulator, and Opti-system for the analysis and enhancement of received signals, our study demonstrates that the proposed MAC protocol surpasses conventional MAC protocols in terms of delay, data throughput, and transmission efficiency.

Research on Destination Network Selection Based on Trajectory Trend

The terminal will switch between different networks to maintain the network service required by the terminal device during the mobile process, and the unreasonable or inaccurate choice of the destination network will lead to the mobile device to switch between multiple networks for many times, resulting in the deterioration of the overall network service quality and the decline of user experience. Based on the trajectory trend, this paper comprehensively considers the terminal moving trajectory and the base station location, reasonably refers to network parameter factors, and combines the analytic hierarchy process to select the destination network, so as to improve the accuracy of network selection, reduce the number of switching times, reduce the efficiency of switching and improve the network utilization.

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.

A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods

Travel time reliability (TTR) serves as a crucial indicator for evaluating the efficiency and service quality of a road traffic network. This paper proposes a multi-task spatio-temporal generative adversarial network (MTST-GAN) model that simultaneously predicts the TTR in morning and evening peak hour periods. The model incorporates multi-graph convolutional networks to extract spatial correlations from travel time data, while long short-term memory neural networks are employed to consider temporal correlations. Additionally, self-attention mechanisms are applied to the proposed MTST-GAN model to further capture spatial and temporal features. A feature fusion bridge is constructed to integrate the spatial and temporal features learned by each task. Through a numerical experiment conducted on a road network in a Chinese city, our findings demonstrate that the proposed model outperforms several state-of-the-art approaches in terms of Jensen-Shannon divergence, mean, standard deviation, and buffer time indices. Finally, we provide conclusions and suggest areas for further research.

Performance Analysis of BGP Dynamic Routing Protocol using QOS for TCP and UDP Services

The Border Gateway Protocol (BGP) is commonly used for TCP and UDP services, but it poses challenges in terms of Quality of Service (QoS) analysis. Parameters like throughput, packet loss, delay, and jitter are crucial for assessing network service quality. This study aims to analyze the performance and influence of the BGP routing protocol on TCP and UDP services using QoS parameters. The research used a GNS3 network simulation to conduct multiple packet transmission tests for TCP and UDP protocols, lasting 15, 30, and 60 seconds; and monitored using Wireshark. For TCP services, the average QoS index value is 3.75, categorizing the quality as "Good". The tested network topology and routing configuration exhibit reliable performance, providing good throughput, low packet loss rates, minimal delays, and stable jitter. Similarly, UDP services demonstrate “Good” performance with an average QoS index of 3.75. The BGP routing protocol in the tested network topology ensures high-quality service with good delivery speed, low packet loss rate, minimal delay, and stable jitter. Overall, the study concludes that the BGP routing protocol effectively provides satisfactory QoS for TCP and UDP services. This research contributes to understanding network performance and optimizing routing protocols for improved telecommunications services. The findings highlight the significance of routing protocols in facilitating efficient data transmission on the Internet, reinforcing the importance of QoS analysis for enhancing service quality.

Optimizing throughput value through re-examining of 4G LTE infrastructure link budgets: A Case Study in Lubuk Kilangan

Broadband wireless access technology has taken the lead in communication technology development, enabling users to communicate with fast data speeds and extensive coverage.. Problems that frequently arise in mobile communication systems are the wide range of services and the level of network service quality. Thus, optimization is carried out to improve network quality in the Lubuk Kilangan area by using the drive test method to determine the quality of the network and bad spot areas and analyzed using terms discovery software. The reason behind the findings of this study is that the optimal link budget value for 4G LTE infrastructure in Lubuk Kilangan is higher than the current industry standard due to the unique characteristics of the area. According to the analysis and calculation of the link budget, power additions, and antenna tilting alterations were carried out for optimization. The power quality of the RSRP parameter increased from the fair (orange) area of 2.58 to 0.29. For the SINR parameter, For the SINR parameter, there was also an increase in quality at poor category locations in the KPI parameters (Range RSRP) locations from 0.08% to 0.05%, resulting in SINR bad spots decreasing. The proportion for Throughput after optimization remains the same, at 100%. Indicating that there had been a good increase in the coverage area, making the coverage in the Lubuk Kilangan area good. It is necessary to conduct a deeper study by comparing several types of antenna orientation, including tilt, depending on various factors such as the deployment scenario, coverage requirements, interference considerations, and the specific characteristics of picocells, femtocells, and repeaters. Future research could explore the impact of other factors, such as network topology and user behavior, on link budget optimization of 4G LTE infrastructure in different contexts

A Novel Approach to Improve Quality of Service in Cloud-Based Content Delivery Network

A content delivery network (CDN) improves normal website functions and increases customer satisfaction. In addition, CDNs foster a seamless online experience, ensuring reliable performance and bolstering cybersecurity measures for enhanced user trust and data protection. It improves High-speed content delivery, Real-time streaming, Multi-user scaling etc. One of the most notable achievements is the remarkable reduction in latency, thanks to advanced CDN technologies that position content closer to end-users, enhancing responsiveness and making real- time applications, online gaming, and interactive content more enjoyable. Furthermore, cost optimization has been a focal point, with CDNs efficiently managing resources and data transfer to deliver content while keeping operational expenses in check. In essence, the ongoing enhancements in CDNs underscore their pivotal role in shaping the digital content landscape. With a focus on speed, scalability, latency reduction, cost efficiency, security, global reach, and data- driven decision-making, CDNs have become indispensable for organizations seeking to excel in serving an increasingly diverse and demanding global audience. In a world where digital content reigns supreme, these improvements empower organizations to meet and exceed user expectations, contributing to their overall success in the digital age. Keywords: Content Delivery Network, Cloud-based Content Delivery Network, Quality metrics and monitoring, Resource management, Performance optimisation.