Confronted with the generalization of monitoring in operational networks, researchers have proposed placement algorithms that can help ISPs deploy their monitoring infrastructure in a cost effective way, while maximizing the benefits of their infrastructure. However, a static placement of monitors cannot be optimal given the short-term and long-term variations in traffic due to re-routing events, anomalies and the normal network evolution. In addition, most ISPs already deploy router embedded monitoring functionalities. Despite some limitations (inherent to being part of a router), these monitoring tools give greater visibility on the network traffic but raise the question on how to configure a network-wide monitoring infrastructure that may contain hundreds of monitoring points.We reformulate the placement problem as follows. Given a network where all links can be monitored, which monitors should be activated and which sampling rate should be set on these monitors in order to achieve a given measurement task with high accuracy and low resource consumption? We provide a formulation of the problem, an optimal algorithm to solve it, and we study its performance on a real backbone network.

We present an analytical model that accounts for the positions of stations with respect to the access point (AP) while evaluating the performance of the 802.11 MAC layer. Our work is based on the Bianchi model where the performance of the 802.11 MAC layer is computed using a discrete time Markov chain, but where all stations are implicitly assumed to be located at the same distance from the AP. In our model, given the position of one station, we compute its saturation throughput while considering the positions of other concurrent stations. Further, our model provides the total saturation throughput of the medium. We solve the model numerically and we show that the saturation throughput per station is strongly dependent not only on the station's position, but also on the positions of the other stations. Results confirm that a station achieves a higher throughput when it is closer to the AP, but bring out that there is a distance threshold above which the throughput decrease is fast and significant. When a station is far from the AP compared to the other stations, it contends for the bandwidth not used by the other stations. We believe that our model is a good tool to dimension 802.11 wireless access networks and to study their capacities and their performances.