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Technique for modelling population movements in a region

IP.com Disclosure Number: IPCOM000029058D
Published in the IP.com Journal: Volume 4 Issue 7 (2004-07-25)
Included in the Prior Art Database: 2004-Jul-25
Document File: 1 page(s) / 34K

Publishing Venue

Siemens

Related People

Juergen Carstens: CONTACT

Abstract

To allow for accurate network planning and traffic engineering, a characterization and generation of realistic workload is important. A proper prediction of the workload within a network has to consider load fluctuations induced by traffic variability as well as by user mobility. To predict the behavior of users, the investigated region of interest is divided into zones which represent homogeneous areas with respect to socio-economic characteristics. Zones are described by multiple properties: number of workplaces, number of residents, etc.. It is proposed to apply the population movement prediction to a real-time data networks. A possible realization of that idea is shown in the figure 1. Here, the traffic engineering endpoints in the core network cooperate with the mobility model to determine future population movements. This information is fed to a network management entity, which can reconfigure traffic engineering tunnels to ensure that the core network can handle future population distributions in the most efficient manner.

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Technique for modelling population movements in a region

Idea: Richard Edwin, GB-Romsey; Andrew Reeve, GB-Romsey; Richard Price, GB-Romsey; Dr. Changpeng Fan, DE-Berlin

To allow for accurate network planning and traffic engineering, a characterization and generation of realistic workload is important. A proper prediction of the workload within a network has to consider load fluctuations induced by traffic variability as well as by user mobility.

To predict the behavior of users, the investigated region of interest is divided into zones which represent homogeneous areas with respect to socio-economic characteristics. Zones are described by multiple properties: number of workplaces, number of residents, etc..

It is proposed to apply the population movement prediction to a real-time data networks. A possible realization of that idea is shown in the figure 1. Here, the traffic engineering endpoints in the core network cooperate with the mobility model to determine future population movements. This information is fed to a network management entity, which can reconfigure traffic engineering tunnels to ensure that the core network can handle future population distributions in the most efficient manner.

When modeling population movements, it has to be determined which zone will people go to. The choice of that zone depends on factors such as where are the people coming from, the distance between the zone of departure and the zone of destination and the time of the day.

A two stage proc...