A QoS-enhanced intelligent stochastic real-time packet scheduler for multimedia IP traffic

Paul, Shuva; Pandit, Malay Kumar

doi:10.1007/s11042-017-4912-6

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…e global search strategy can use the genetic algorithm and can also use other global search algorithms. e meme algorithm combines the advantages of the local search algorithm and the global search algorithm [17]. It uses the local search strategy to realize the local depth search of the individual and adopts the global search strategy to complete the breadth search of the population.…”

Section: Related Workmentioning

confidence: 99%

Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

Chen

2022

Mathematical Problems in Engineering

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The search range and search position of the particle are closely related to the shrinkage-expansion factor of the particle and the value of the center of the potential well. As the number of iterations increases, its search will gradually fall near the center of the potential well, and the search range will gradually decrease. Therefore, when the center of the potential well and the search range gradually approach the global optimum, the final result of the algorithm can be guaranteed to be the global optimum. Therefore, we need to optimize the shrinkage-expansion factor and the potential well center, improve the individual development ability of the group in the later stage of the algorithm and the expansion ability of the group, so that the potential well center is easier to fall near the global optimal point. Based on the chaotic strategy, a particle swarm initialization method is proposed. Using the unique ergodicity of the chaotic system, the method can make the particles spread well in the solution space of the search problem. Based on the antecedent normal cloud model, an adaptive method for determining control parameters and the center of the potential well is proposed. Based on the consequent normal cloud model, a particle adaptive mutation method is proposed. The comprehensive application of these improvement measures to the improved QPSO effectively improves the performance of the original algorithm. In this paper, the principle and method of multioptical axis parallelism measurement are studied. Based on the hardware equipment of the parallelism measurement system based on the large-diameter off-axis parabolic mirror collimator, the measurement accuracy and automation of the system are improved through image processing technology. The degree and anti-interference ability of the system are analyzed in detail. According to the characteristics of the system, an optimized focus evaluation function and a center positioning algorithm are proposed to improve the measurement speed and accuracy of the system. Through the improvement of the system DCT focus evaluation coefficient method and the optimization of the least squares ellipse fitting process, the measurement accuracy and anti-interference ability of the system are improved, the calculation time is shortened, and the real-time performance and automation of the system are enhanced.

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Section: Related Workmentioning

confidence: 99%

Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

Chen

2022

Mathematical Problems in Engineering

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“…Multimedia transmission in both real-time and non-real-time requires different QoS metrics that include throughput, latency, jitter, packet loss rate, and bit-error rate [32], [33]. The quality of multimedia services strictly depends on these types of performance characteristics.…”

Section: Multimedia Services and Crnsmentioning

confidence: 99%

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

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“…Once the type of multimedia traffic is identified, its QoS could be better guaranteed [5]. For example, Suman et al [6] proposed a real-time optimal fair packet scheduler for IP routers to maximize the QoS of multimedia traffic including Voice over Internet Protocol (VoIP), Internet Protocol Television (IPTV) and so forth. While in this scheduler system, one important premise is to implement accurate classification on these multimedia traffic.…”

Section: Introductionmentioning

confidence: 99%

Multimedia Traffic Classification for Imbalanced Environment

Yang

Jin

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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With ever-increasing volume and variety of multimedia traffic on the Internet, machine learning-empowered techniques nowadays tend to become indispensable for future intelligent network management. To realize automatic traffic management with Quality of Service (QoS) guarantees, there is a pressing need for accurate traffic classification. However, the inherent characteristics of networks cause imbalanced class distribution in traffic classification, which could degrade the performance of classification, especially on the minority classes. To address the issue of class imbalance in both stationary and nonstationary environments, this paper proposes a novel scheme called CHS (chain hierarchical structure) which is able to characterize class distribution from a new perspective. By building an error model, we can compute the error propagation generated by CHS and analyze the factors that affect it. More importantly, two key methods involving classifier ranking and combination with the hierarchical structure are devised to mitigate the error propagation produced by the classifier. The effectiveness of the developed framework is validated through experiments over two real-world traffic datasets in both stationary and nonstationary environments. The experimental results demonstrate that our proposed methods outperform the state-of-the-art approaches in terms of classification accuracy and running time. The proposed methods are particularly effective in the nonstationary imbalanced environment.

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A QoS-enhanced intelligent stochastic real-time packet scheduler for multimedia IP traffic

Cited by 9 publications

References 34 publications

Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Multimedia Traffic Classification for Imbalanced Environment

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