### Abstract

Original language | English |
---|---|

Publisher | IEEE Computer Society |

Number of pages | 10 |

Place of Publication | USA |

ISBN (Print) | 978-0-7695-4188-4 |

DOIs | |

Publication status | Published - Sep 2010 |

Externally published | Yes |

### Fingerprint

### Keywords

- METIS-276245
- EWI-19152
- IR-75339

### Cite this

*USA: IEEE Computer Society*. https://doi.org/10.1109/QEST.2010.38

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*Automating the mean-field method for large dynamic gossip networks*. IEEE Computer Society, USA. https://doi.org/10.1109/QEST.2010.38

**Automating the mean-field method for large dynamic gossip networks.** / Bakhshi, Rena; Endrullis, Jörg; Endrullis, Stefan; Fokkink, Wan; Haverkort, Boudewijn R.H.M.

Research output: Other contribution

TY - GEN

T1 - Automating the mean-field method for large dynamic gossip networks

AU - Bakhshi, Rena

AU - Endrullis, Jörg

AU - Endrullis, Stefan

AU - Fokkink, Wan

AU - Haverkort, Boudewijn R.H.M.

N1 - 10.1109/QEST.2010.38

PY - 2010/9

Y1 - 2010/9

N2 - We investigate an abstraction method, called mean- field method, for the performance evaluation of dynamic net- works with pairwise communication between nodes. It allows us to evaluate systems with very large numbers of nodes, that is, systems of a size where traditional performance evaluation methods fall short. While the mean-field analysis is well-established in epidemics and for chemical reaction systems, it is rarely used for commu- nication networks because a mean-field model tends to abstract away the underlying topology. To represent topological information, however, we extend the mean-field analysis with the concept of classes of states. At the abstraction level of classes we define the network topology by means of connectivity between nodes. This enables us to encode physical node positions and model dynamic networks by allowing nodes to change their class membership whenever they make a local state transition. Based on these extensions, we derive and implement algorithms for automating a mean-field based performance evaluation.

AB - We investigate an abstraction method, called mean- field method, for the performance evaluation of dynamic net- works with pairwise communication between nodes. It allows us to evaluate systems with very large numbers of nodes, that is, systems of a size where traditional performance evaluation methods fall short. While the mean-field analysis is well-established in epidemics and for chemical reaction systems, it is rarely used for commu- nication networks because a mean-field model tends to abstract away the underlying topology. To represent topological information, however, we extend the mean-field analysis with the concept of classes of states. At the abstraction level of classes we define the network topology by means of connectivity between nodes. This enables us to encode physical node positions and model dynamic networks by allowing nodes to change their class membership whenever they make a local state transition. Based on these extensions, we derive and implement algorithms for automating a mean-field based performance evaluation.

KW - METIS-276245

KW - EWI-19152

KW - IR-75339

U2 - 10.1109/QEST.2010.38

DO - 10.1109/QEST.2010.38

M3 - Other contribution

SN - 978-0-7695-4188-4

PB - IEEE Computer Society

CY - USA

ER -