01965nas a2200157   4500000000100000008004100001260004400042100002000086700001700106700002900123700002000152700001600172245008700188856005200275520148000327       2019                            d  c04/2019bIFIP/IEEEaWashington, DC, USA1 aKhulan Batbayar1 aRoc Meseguer1 aEmmanouil Dimogerontakis1 aLeandro Navarro1 aRamin Sadre00aCollaborative informed gateway selection in large-scale and heterogeneous networks  uhttp://dl.ifip.org/db/conf/im/im2019/189253.pdf3 aIn wireless community access networks, clients tend to reach the Internet through multiple gateway nodes instead of a single default gateway. The mapping of gateways to clients should take into account the perception of network performance from each client node. Network conditions and traffic load can fluctuate and make repeated client-gateway measurements necessary. However, frequent measurements would result in a high communication overhead as well as high processing overhead in gateways and clients. We propose a lightweight client-side gateway selection algorithm by crowd-sourcing monitoring information from neighbor clients, without requiring explicit topology information or a detailed view of the network, while providing an accurate selection as compared to an ideal omniscient approach. Our collaborative gateway selection algorithm achieves good end-to-end performance, such as low latency perceived at client nodes, and fair distribution of the measurements over the gateway nodes. The number of performance measurements triggered by clients are reduced drastically, from n down to 2 measurements per node in each period. An experimental evaluation of our approach shows more than 80% similarity estimation of the gateway performance in the majority of the considered cases. We propose two variants of the gateway selection algorithm, collaborative-best and collaborative-fair, which yield near optimal gateway selection while utilizing partial information.