QoS adaptation in real time systems based on CBWFQ

Badr, Shaimaa; Bayoumi, Fatma; Darwesh, Gamal

doi:10.1109/nrsc.2011.5873626

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…The behavior of queues and their traffic is monitored to address the buffer under-run problem. Shaimaa et al [13] have demonstrated that the combination of Class-Based Weighted Fair Queuing and Low Latency Queuing (CB-WFQ-LLQ) improved the performance of multimedia applications. A scheduling algorithm that categorizes and prioritizes the real-time traffic was developed [14].…”

Section: Related Workmentioning

confidence: 99%

The Performance Analysis of Low Latency Queueing Scheduling Algorithm for MANETs

2023

PSEN

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Delay is a major Quality of Service (QoS) metric in Mission Critical Applications and some of these include health, vehicle and inspection safety applications. Some of such applications run on Mobile Ad Hoc Network (MANET) set ups which comes with transmission challenges arising from the size of traffic packets, environmental conditions and others. These challenges cause transmission delays, packet loss and hence a degraded network performance. In this article we study a Low Latency Queueing (LLQ) Scheduling Algorithm that makes use of three priority queues each transmitting voice, video and text packets. For the purposes of improving delay performance piggy backing off video packets on voice transmission is used. The LLQ model is developed under two scenarios as follows: (I) when voice packet is delayed once and piggybacked with video on transmission. (II) when voice packet is delayed only if there is a partial video packet being transmitted. During scheduling of traffic voice packets are combined with the partial video packet. We investigate the performance variation of the LLQ in an M/G/1 queue under different scenarios and under two service distributions namely: Exponential and Bounded Pareto (BP). The numerical results for the first scenario revealed that the video packets experienced the least conditional mean response time/conditional mean slowdown, followed by voice and least were text packets under LLQ Algorithm. While for second scenario, it was observed that voice packets experienced the least conditional mean response time/conditional mean slowdown, followed by video packets and then text packets in that order under LLQ Algorithm.

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Section: Related Workmentioning

confidence: 99%

The Performance Analysis of Low Latency Queueing Scheduling Algorithm for MANETs

2023

PSEN

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“…CBWFQ is especially beneficial in scenarios where various types of traffic, such as voice, video, and data, coexist on a single network link. It guarantees that each class receives the necessary resources to fulfill its QoS demands [4]. The CAIDA Anonymized Internet Traces dataset is an invaluable asset in the field of network research.…”

Section: Introductionmentioning

confidence: 99%

QoS-Aware Routing and Resource Allocation Techniques for Enhanced Network Performance

Pradeep Kundlik Deshmukh

2024

jes

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The importance of Quality of Service (QoS) remains of utmost importance in the endeavor to provide high-quality network services. This study focuses on the important area of Quality of Service (QoS) in network services. Specifically, it explores QoS-Aware Routing and Resource Allocation techniques, with a particular emphasis on Class-Based Weighted Fair Queuing (CBWFQ). Our research utilizes the NS-3 simulator to thoroughly assess network performance by analyzing crucial parameters such as latency, throughput, and reliability. We draw insights from the CAIDA Anonymized Internet Traces dataset. CBWFQ, an advanced queuing mechanism, is highlighted for its capability to intelligently categorize and prioritize network traffic into separate classes, each with customized weightings and resource guarantees. The outcomes derived from our experimentation demonstrate significant enhancements in latency, throughput, and reliability across various scenarios, confirming the efficacy of CBWFQ in optimizing resource allocation and guaranteeing superior QoS. This research not only tackles the immediate difficulties encountered by network administrators, but also provides valuable insights for service providers and researchers aiming to enhance network performance in the face of diverse traffic patterns. In addition, we propose potential areas for future investigation, including the examination of AI-driven QoS mechanisms and adaptable strategies that can effectively navigate the constantly changing network environments. The incorporation of QoS methodologies with cutting-edge technologies, such as 5G and future iterations, presents a promising opportunity to improve network management and performance in the upcoming era.

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“…From the application viewpoint, the QoS indicates the quality of the application observed by the end-user. Whereas, the viewpoint of the network is the service quality provided by the network to the applications in terms of the network QoS parameters, such as throughput, latency, and reliability [13]. The QoS factors depending on the network metrics such as bandwidth, delay, and packet loss so as the throughput demanding an adequate bandwidth, low delay and, low packets loss ratio to achieve a better application quality.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Quality-of-Service Allocation Scheme for Improving Video Quality over a Wireless Network

Alsaqour¹,

Hadi²,

Abdelhaq³

2022

Intelligent Automation &Amp; Soft Computing

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QoS adaptation in real time systems based on CBWFQ

Cited by 8 publications

References 5 publications

The Performance Analysis of Low Latency Queueing Scheduling Algorithm for MANETs

The Performance Analysis of Low Latency Queueing Scheduling Algorithm for MANETs

QoS-Aware Routing and Resource Allocation Techniques for Enhanced Network Performance

Adaptive Quality-of-Service Allocation Scheme for Improving Video Quality over a Wireless Network

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