|Vamseedhar R Reddyvari Raja||Texas A&M University, USA|
|Srinivas G Shakkottai||Texas A&M University, USA|
|Parimal Parag||Indian Institute of Science, India|
The ability of a P2P network to scale its throughput up in proportion to the arrival rate of peers has recently been shown to be crucially dependent on the chunk sharing policy employed. Some policies can result in low frequencies of a particular chunk, known as the missing chunk syndrome, which can dramatically reduce throughput and lead to instability of the system. For instance, commonly used policies that nominally "boost" the sharing of infrequent chunks such as the well-known rarest-first algorithm have been shown to be unstable. Recent efforts have largely focused on the careful design of boosting policies to mitigate this issue. We take a complementary viewpoint, and instead consider a policy that simply prevents the sharing of the most frequent chunk(s). Following terminology from statistics wherein the most frequent value in a data set is called the mode, we refer to this policy as mode suppression. We prove the stability of this algorithm using Lyapunov techniques. We also design a distributed version that suppresses the mode via an estimate obtained by sampling three randomly selected peers. We show numerically that both algorithms perform well at minimizing total download times, with distributed mode suppression outperforming all others that we tested against.