  Packets originated from an information source in the network can be highly
correlated. These packets are often routed through different paths, and
compressing them requires to process them individually. Traditional universal
compression solutions would not perform well over a single packet because of
the limited data available for learning the unknown source parameters. In this
paper, we define a notion of correlation between information sources and
characterize the average redundancy in universal compression with side
information from a correlated source. We define the side information gain as
the ratio between the average maximin redundancy of universal compression
without side information to that with side information. We derive a lower bound
on the side information gain, where we show that the presence of side
information provides at least 50% traffic reduction over traditional universal
compression when applied to network packet data confirming previous empirical
studies.
