  Several studies have identified a significant amount of redundancy in the
network traffic. For example, it is demonstrated that there is a great amount
of redundancy within the content of a server over time. This redundancy can be
leveraged to reduce the network flow by the deployment of memory units in the
network. The question that arises is whether or not the deployment of memory
can result in a fundamental improvement in the performance of the network. In
this paper, we answer this question affirmatively by first establishing the
fundamental gains of memory-assisted source compression and then applying the
technique to a network. Specifically, we investigate the gain of
memory-assisted compression in random network graphs consisted of a single
source and several randomly selected memory units. We find a threshold value
for the number of memories deployed in a random graph and show that if the
number of memories exceeds the threshold we observe network-wide reduction in
the traffic.
