  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 also
consider a more general version that suppresses the mode only if the mode
frequency is larger than the lowest frequency by a fixed threshold. We prove
the stability of mode-suppression using Lyapunov techniques, and use a Kingman
bound argument to show that the total download time does not increase with peer
arrival rate. We then design versions of mode-suppression that sample a small
number of peers at each time, and construct noisy mode estimates by aggregating
these samples over time. We show numerically that the variants of
mode-suppression yield near-optimal download times, and outperform all other
recently proposed chunk sharing algorithms.
