This paper makes the case for "Room-Area Networks" (RAN), a new category that falls between personal area networks and local area networks. In a RAN, a set of nodes can hear each other only if they are in the same room, broadly construed as being within earshot. We define a RAN abstraction, and we present example applications ranging from social contact management to building automation to gaming where this abstraction will help. The requirements of a RAN are poorly served by current technologies such as Bluetooth, near-field communication (NFC), Wi-Fi, and infrared. Acoustic channels, on the other hand, are well-suited in principle for effective propagation within human earshot and sharp attenuation at room boundaries. We provide a portable reference implementation of an 802.11a-like physical layer for the acoustic medium that works on current mobile devices, with successful communication even in noisy environments at distances over 8 meters.

This demo presents a measurement toolkit, Mahimahi, that records websites and replays them under emulated network conditions. Mahimahi is structured as a set of arbitrarily composable UNIX shells. It includes two shells to record and replay Web pages, RecordShell and ReplayShell, as well as two shells for network emulation, DelayShell and LinkShell. In addition, Mahimahi includes a corpus of recorded websites along with benchmark results and link traces (https://github.com/ravinet/sites).

Cloud computing infrastructures are increasingly being used by network-intensive applications that transfer significant amounts of data between the nodes on which they run. This paper shows that tenants can do a better job placing applications by understanding the underlying cloud network as well as the demands of the applications. To do so, tenants must be able to quickly and accurately measure the cloud network and profile their applications, and then use a network-aware placement method to place applications. This paper describes Choreo, a system that solves these problems. Our experiments measure Amazon's EC2 and Rackspace networks and use three weeks of network data from applications running on the HP Cloud network. We find that Choreo reduces application completion time by an average of 8%-14% (max improvement: 61%) when applications are placed all at once, and 22%-43% (max improvement: 79%) when they arrive in real-time, compared to alternative placement schemes.