Queuing theory for network modeling and performance evaluation

2018 ◽  
pp. 255-265
Keyword(s):  
Performance Evaluation ◽  
Queuing Theory ◽  
Network Modeling ◽  
And Performance

Signaling Packet Aggregation and Compression in SIP Network: Modeling and Performance Evaluation

2019 ◽  
Vol 110 (2) ◽  
pp. 651-676
Author(s):  
Garima Mishra ◽  
Anupam Gautam ◽  
S. Dharmaraja ◽  
Subrat Kar
Keyword(s):  
Performance Evaluation ◽  
Network Modeling ◽  
Packet Aggregation ◽  
And Performance

scholarly journals Network Modeling and Performance Evaluation for G.fast

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Driton Statovci ◽  
Per Odling ◽  
Chao Zhang ◽  
Christoph Mecklenbrauker
Keyword(s):  
Performance Evaluation ◽  
Network Modeling ◽  
And Performance

Modeling and Performance Evaluation

2015 ◽  
pp. 746-769
Author(s):  
Yuehong Gao ◽  
Zhidu Li ◽  
Guoting Zhang ◽  
He Bai
Keyword(s):  
Markov Chain ◽  
Performance Evaluation ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Queuing Theory ◽  
Deep Understanding ◽  
Radio Networks ◽  
Network Calculus ◽  
And Performance ◽  
Stochastic Network Calculus

Performance evaluation and analysis are of key importance to obtain deep understanding of cognitive radio networks. Some effects have been made to model and analyze the performance of cognitive radio networks. In the literature, there are two methodologies: queuing theory/Markov chain-based analysis and stochastic network calculus-based analysis. These two methodologies rely on different mathematical basics and modeling approaches. Thus, they lead to different output metrics on various viewpoints. This chapter aims to give an overall introduction to both methodologies. First, the fundamental models used in queuing/Markov chain-based analysis are presented, followed by their applications in cognitive radio networks. Then, network calculus basics are introduced with the modeling and application in performance analysis of the cognitive radio network.

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