Packet loss rate mapped to the quality of experience

2016 ◽  
Vol 77 (1) ◽  
pp. 387-422 ◽  
Author(s):  
Jin Wang ◽  
Yi bin Hou
Keyword(s):  
Packet Loss ◽  
Quality Of Experience ◽  
Loss Rate ◽  
Packet Loss Rate

Experimental Research on Diffserv-Aware MPLS Traffic Engineering

2012 ◽  
Vol 263-266 ◽  
pp. 1858-1863
Author(s):  
Jin He Zhou ◽  
Guo Min Xia
Keyword(s):  
Quality Of Service ◽  
Experimental Research ◽  
Packet Loss ◽  
Traffic Engineering ◽  
High Performance ◽  
Loss Rate ◽  
Differentiated Services ◽  
Packet Loss Rate ◽  
Differentiated Service

Diffserv-aware and Traffic Engineering combine the advantages of MPLS, Traffic Engineering (TE) and Differentiated Services (Diffserv, DS) to provide high performance and Quality of Service(QoS) in networks. We have designed three scenarios on Juniper Networks platforms to analyze the packet loss rate and delay for video, voice and data. The results show that MPLS DS-TE can improve the QoS for differentiated service effectively. The research has practical value for the development of DS-TE based on MPLS.

scholarly journals A Statistical Approach to Adaptive Playout Scheduling in Voice Over Internet Protocol Communication

2018 ◽  
Vol 8 (5) ◽  
pp. 2926
Author(s):  
Priya Chandran ◽  
Chelpa Lingam
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Voice Quality ◽  
Internet Protocol ◽  
Packet Loss Rate ◽  
Voice Over Internet Protocol ◽  
Network Delay ◽  
Voice Communication ◽  
Voice Packets

Factors like network delay, latency and bandwidth significantly affect the quality of communication using Voice over Internet Protocol. The use of jitter buffer at the receiving end compensates the effect of varying network delay up to some extent. But the extra buffer delay given for each packet plays a major role in playing late packets and thereby improving voice quality. As the buffer delay increases packet loss rate decreases, which in general is a very good sign. However, an increase of buffer delay beyond a certain limit affects the interactive quality of voice communication. In this paper, we propose a statistical framework for adaptive playout scheduling of voice packets based on network statistics, packet loss rate and availability of packets in the buffer. Experimental results show that the proposed model allocates optimal buffer delay with the lowest packet loss rate when compared with other algorithms.

scholarly journals Genetic Algorithm Based QoS Perception Routing Protocol for VANETs

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Guoan Zhang ◽  
Min Wu ◽  
Wei Duan ◽  
Xinming Huang
Keyword(s):  
Genetic Algorithm ◽  
Routing Protocol ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Loss Rate ◽  
Ad Hoc ◽  
Packet Loss Rate ◽  
Qos Requirement ◽  
Connectivity Aware Routing

A genetic algorithm (GA) based QoS perception routing protocol (GABR) is proposed to guarantee the quality of service (QoS) influenced by broken links between vehicles and the failure of packets transmission in a vehicular ad hoc network (VANET). With the observation that all improvable paths are probed by the intersection based routing protocol, the genetic GA is utilized to optimize the global available paths which satisfies the QoS requirement. Moreover, by means of the numerical results, it is shown that the proposed scheme is significantly improved compared with protocols of the intersection based routing (IBR) and connectivity aware routing (CAR) in terms of transmission delay and packet loss rate.

Packet loss rate monitoring model of IOT based on differential evolution algorithm

2021 ◽  
pp. 1-12
Author(s):  
Yinghua Feng ◽  
Wei Yang
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Packet Loss ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Packet Loss Rate ◽  
Data Space ◽  
Monitoring Model ◽  
Evolution Algorithm ◽  
The Internet Of Things

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IOT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

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