  In order to scale economically, data centers are increasingly evolving their
data storage methods from the use of simple data replication to the use of more
powerful erasure codes, which provide the same level of reliability as
replication but at a significantly lower storage cost. In particular, it is
well known that Maximum-Distance-Separable (MDS) codes, such as Reed-Solomon
codes, provide the maximum storage efficiency. While the use of codes for
providing improved reliability in archival storage systems, where the data is
less frequently accessed (or so-called "cold data"), is well understood, the
role of codes in the storage of more frequently accessed and active "hot data",
where latency is the key metric, is less clear.
  In this paper, we study data storage systems based on MDS codes through the
lens of queueing theory, and term this the "MDS queue." We analytically
characterize the (average) latency performance of MDS queues, for which we
present insightful scheduling policies that form upper and lower bounds to
performance, and are observed to be quite tight. Extensive simulations are also
provided and used to validate our theoretical analysis. We also employ the
framework of the MDS queue to analyse different methods of performing so-called
degraded reads (reading of partial data) in distributed data storage.
