Summary form only given, as follows. The Internet is a pervasive medium for content distribution. It is estimated that in 2015, it will take us five years to view all video crossing IP networks each second. When new content is made available, file distribution systems often have to cope with a sudden surge in the number of users, called flash crowd, without loss of download performance. Flash crowds can have serious consequences on business revenue. For example, Amazon estimates that its sales decrease by 1% for every 100ms delay due to flash crowds, and Google reports that half a second increase in waiting time results in a 20% decrease in traffic. This keynote discusses a new approach in modeling the performance of peer-assisted file distribution systems with flash crowds. Peerassisted file distribution systems extend client-server systems. The peers download a file as a client but at the same time act like servers by sharing the file across peers, thus improving the overall system download performance. This keynote is divided into three main parts. First, we review three main approaches in analysis flash crowd performance, namely, measurement, simulation and analytic models. Secondly, based on insights drawn from extensive measurement studies, we propose a general analytical model for understanding flash crowd performance. We show that the utilization of available peer bandwidth over the duration of flash crowd can be characterized by three distinct phases called startup, maximum utilization and end-game. We discuss the applications of our model by users, service providers and protocols designers of peer-assisted file distribution systems. In conclusion, we highlight a number of challenges and opportunities in modeling flash crowd performance in web-based and mobile applications.
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