Separation of data and presentation for the next generation Internet using the four-tier architecture

2002 ◽  
Author(s):  
R. Behravanfar
Keyword(s):  
Next Generation ◽  
Next Generation Internet

Research and Exploration of Next-Generation Internet Architecture

2009 ◽  
Vol 31 (9) ◽  
pp. 1536-1548 ◽  
Author(s):  
Jian-Ping WU ◽  
Qian WU ◽  
Ke XU
Keyword(s):  
Next Generation ◽  
Internet Architecture ◽  
Next Generation Internet

A Next Generation Internet Demonstration

2000 ◽  
Vol 11 (5) ◽  
pp. 52
Author(s):  
BOB JOPSON
Keyword(s):  
Next Generation ◽  
Next Generation Internet

scholarly journals An IoMT based cyber training framework for orthopedic surgery using Next Generation Internet technologies

2018 ◽  
Vol 12 ◽  
pp. 128-137 ◽  
Author(s):  
J. Cecil ◽  
Avinash Gupta ◽  
Miguel Pirela-Cruz ◽  
Parmesh Ramanathan
Keyword(s):  
Orthopedic Surgery ◽  
Next Generation ◽  
Internet Technologies ◽  
Next Generation Internet

scholarly journals Comprehensive Downtime Prediction in Next-Generation Internet

1970 ◽  
Vol 5 (1) ◽  
Author(s):  
W. F. Al-Khateeb, S. Al-Irhayim, and K. A. Al-Khateeb
Keyword(s):  
Analytical Calculation ◽  
Multiprotocol Label Switching ◽  
Next Generation ◽  
Label Switching ◽  
Next Generation Internet ◽  
Reliability Parameters ◽  
Wide Range ◽  
The Individual ◽  
Single Figure

The benchmark for the reliability quality of networks depends mainly on the accuracy and comprehensiveness of the reliability parameters. Downtime prediction of a communication system is crucial for the quality of service (QoS) offered to the end-user. Markov model enables analytical calculation of average single figure cumulative downtime over one year. The single average approach, generally, does not adequately describe the wide range of service performance that is likely to be experienced in communications systems due to the random nature of the failure. Therefore, it would be more appropriate to add downtime distribution obtained from network availability models to predict the expected cumulative downtime and other performance parameters among a large number of system populations. The distribution approach provides more comprehensive information about the behavior of the individual systems. Laplace-Stieltjes transform enables analytical solutions for simple network architectures, i.e. the simplex system and the parallel system. This paper uses simulations to determine reliability parameters for complex architecture such as the Multiprotocol Label Switching (MPLS) backbone planned for next-generation Internet. In addition to the single figure downtime, simulations provide other reliability parameters such as probability of zero downtime. The paper also considers the downtime distribution among a population of equally designed systems.Key Words: Reliability, availability, downtime, Multiprotocol label switching

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