  Erasure codes, such as Reed-Solomon (RS) codes, are being increasingly
employed in data centers to combat the cost of reliably storing large amounts
of data. Although these codes provide optimal storage efficiency, they require
significantly high network and disk usage during recovery of missing data. In
this paper, we first present a study on the impact of recovery operations of
erasure-coded data on the data-center network, based on measurements from
Facebook's warehouse cluster in production. To the best of our knowledge, this
is the first study of its kind available in the literature. Our study reveals
that recovery of RS-coded data results in a significant increase in network
traffic, more than a hundred terabytes per day, in a cluster storing multiple
petabytes of RS-coded data.
  To address this issue, we present a new storage code using our recently
proposed "Piggybacking" framework, that reduces the network and disk usage
during recovery by 30% in theory, while also being storage optimal and
supporting arbitrary design parameters. The implementation of the proposed code
in the Hadoop Distributed File System (HDFS) is underway. We use the
measurements from the warehouse cluster to show that the proposed code would
lead to a reduction of close to fifty terabytes of cross-rack traffic per day.
