Name
Abstract | ||
|---|---|---|
Fluid models have for some time been used to approximate stochastic networks with discrete state. These range from traditional 'heavy traffic' approximations to the recent advances in bio-chemical system models. Here we use an approximate compositional method to analyse a simple feedforward network of fluid queues which comprises both probabilistic branching and superposition. This extends our earlier work that showed the approximation to yield excellent results for a linear chain of fluid queues. The results are compared with those from a simulation model of the same system. The compositional approach is shown to yield good approximations, deteriorating for nodes with high load when there is correlation between their immediate inputs. This correlation arises when a common set of external sources feeds more than one queue, directly or indirectly. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1330555.1330568 | SIGMETRICS Performance Evaluation Review |
Keywords | Field | DocType |
fluid model,common set,approximate stochastic network,compositional approach,excellent result,earlier work,fluid queue,approximate analysis,bio-chemical system model,discrete state,approximate compositional method,system modeling,simulation model | Superposition principle,Computer science,Queue,Fluid queue,Processor sharing,Real-time computing,Probabilistic logic,Fork–join queue,Distributed computing,Feed forward,Branching (version control) | Journal |
Volume | Issue | Citations |
35 | 2 | 1 |
PageRank | References | Authors |
0.50 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tony Field | 1 | 144 | 18.45 |
| Peter Harrison | 2 | 73 | 7.58 |