Revisiting stochastic loss networks

2008 ◽  
Author(s):  
Kyomin Jung ◽  
Yingdong Lu ◽  
Devavrat Shah ◽  
Mayank Sharma ◽  
Mark S. Squillante
Keyword(s):  
Loss Networks

On throughput in stochastic linear loss networks

2008 ◽  
Vol 36 (2) ◽  
pp. 125-127 ◽  
Author(s):  
Petar Momčilović ◽  
Mark S. Squillante
Keyword(s):  
Loss Networks ◽  
Linear Loss

Reduced load approximations for loss networks

1993 ◽  
Vol 2 (1) ◽  
pp. 21-50 ◽  
Author(s):  
Andrew J. Coyle ◽  
William Henderson ◽  
Peter G. Taylor
Keyword(s):  
Loss Networks

Sequential Gradient Coding for Packet-Loss Networks

2021 ◽  
pp. 1-1
Author(s):  
M. Nikhil Krishnan ◽  
Erfan Hosseini ◽  
Ashish Khisti
Keyword(s):  
Packet Loss ◽  
Loss Networks

Limit theorems for loss networks with diverse routing

1989 ◽  
Vol 21 (04) ◽  
pp. 804-830 ◽  
Author(s):  
I. B. Ziedins ◽  
F.P. Kelly
Keyword(s):  
Information Systems ◽  
Asymptotic Analysis ◽  
Limit Theorems ◽  
Loss Networks ◽  
Exact Analysis ◽  
Large Numbers

Loss networks have long been of interest to telephone engineers, and are becoming increasingly important as models of computer and information systems. In this paper we present an asymptotic analysis of loss networks exhibiting various forms of symmetry. Our aim is to better understand the behaviour of networks involving very large numbers of links and routes, where an exact analysis is not possible and approximations are required.

scholarly journals Asymptotic Blocking Probabilities in Loss Networks with Subexponential Demands

2007 ◽  
Vol 44 (04) ◽  
pp. 1088-1102 ◽  
Author(s):  
Yingdong Lu ◽  
Ana Radovanović
Keyword(s):  
Information Systems ◽  
Point Processes ◽  
Communications Networks ◽  
Loss Networks ◽  
Blocking Probabilities ◽  
Asymptotic Expressions ◽  
Simulation Results ◽  
Erlang Formula

The analysis of stochastic loss networks has long been of interest in computer and communications networks and is becoming important in the areas of service and information systems. In traditional settings computing the well-known Erlang formula for blocking probabilities in these systems becomes intractable for larger resource capacities. Using compound point processes to capture stochastic variability in the request process, we generalize existing models in this framework and derive simple asymptotic expressions for the blocking probabilities. In addition, we extend our model to incorporate reserving resources in advance. Although asymptotic, our experiments show an excellent match between derived formulae and simulation results even for relatively small resource capacities and relatively large values of the blocking probabilities.

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