  Scientific collaborations are increasingly relying on large volumes of data
for their work and many of them employ tiered systems to replicate the data to
their worldwide user communities. Each user in the community often selects a
different subset of data for their analysis tasks; however, members of a
research group often are working on related research topics that require
similar data objects. Thus, there is a significant amount of data sharing
possible. In this work, we study the access traces of a federated storage cache
known as the Southern California Petabyte Scale Cache. By studying the access
patterns and potential for network traffic reduction by this caching system, we
aim to explore the predictability of the cache uses and the potential for a
more general in-network data caching. Our study shows that this distributed
storage cache is able to reduce the network traffic volume by a factor of 2.35
during a part of the study period. We further show that machine learning models
could predict cache utilization with an accuracy of 0.88. This demonstrates
that such cache usage is predictable, which could be useful for managing
complex networking resources such as in-network caching.
