Minimum Queue Length Load-Balancing in Planned Wireless Mesh Networks
Resumen:
Wireless Mesh Networks (WMNS) have emerged in the last years as a cost-efficient alternative to traditional wired access networks. In order to fully exploit the intrinsically scarce resources WMNS possess, the use of dynamic routing has been proposed. We argue instead in favour of separating routing from forwarding (i.e. a la MPLS) and implementing a dynamic load-balancing scheme that forwards incoming packets along several pre-established paths in order to minimize a certain congestion function. In this paper, we consider a particular but very important scenario: a planned WMN where all bidirectional point-to-point links do not interfere with each other. Due to its versatility and simplicity, we use the sum over all links of the mean queue length as congestion function. A method to learn this function from measurements is presented, whereas simulations illustrate the framework.
2012 | |
Routing Logic gates Wireless communication Optimization Queueing analysis IEEE 802.11 Standards Load modeling Telecomunicaciones |
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Inglés | |
Universidad de la República | |
COLIBRI | |
https://hdl.handle.net/20.500.12008/41136 | |
Acceso abierto | |
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
Sumario: | Wireless Mesh Networks (WMNS) have emerged in the last years as a cost-efficient alternative to traditional wired access networks. In order to fully exploit the intrinsically scarce resources WMNS possess, the use of dynamic routing has been proposed. We argue instead in favour of separating routing from forwarding (i.e. a la MPLS) and implementing a dynamic load-balancing scheme that forwards incoming packets along several pre-established paths in order to minimize a certain congestion function. In this paper, we consider a particular but very important scenario: a planned WMN where all bidirectional point-to-point links do not interfere with each other. Due to its versatility and simplicity, we use the sum over all links of the mean queue length as congestion function. A method to learn this function from measurements is presented, whereas simulations illustrate the framework. |
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