Optimizing mobility support in large switched LANs

With the recent explosion in wireless mobile communication, a great deal of work is taking place to solve the problem of handling mobile users in the Internet. This problem encompasses the design of schemes for tracking and routing to mobile users in various networks and subnetworks that make up the Internet infrastructure. Switched LANs today employ the transparent learning protocol which allows switches to learn about the location of users by promiscuously listening to the traffic emitted by the users. Unfortunately, as this paper shows, fast moving users render the transparent learning protocol inefficient so that high bandwidth consumption and even packet loss may occur. To prevent packets from being mis-routed or even lost, one can employ a lightweight control-based mechanism for user tracking, based on an existing LAN-protocol known as the generic attribute registration protocol (GARP). However, this solution can lead to large databases at the switches, which can in turn significantly increase the cost of the Internet infrastructure. In this paper, we propose to combine and tune the transparent learning protocol and the GARP protocol in an efficient way as to jointly minimize packet loss, bandwidth consumption and the size of the tracking databases in a large switched network. Our optimization is shown to be effective for a wide range of mobility speeds and application characteristics, and consistent with the state of technology.