Enhancing scalability in distributed storage systems with Cauchy Reed-Solomon codes

System scaling becomes essential and indispensable for distributed storage systems due to the explosive growth of data volume. As fault-protection is also a necessity in large-scale distributed storage systems, and Cauchy Reed-Solomon (CRS) codes are widely deployed to tolerate multiple simultaneous node failures, this paper studies the scaling of distributed storage systems with CRS codes. In particular, we formulate the scaling problem with an optimization model in which both the post-scaling encoding matrix and the data migration policy are assumed to be unknown in advance. To minimize the I/O overhead for CRS scaling, we first derive the optimal post-scaling encoding matrix under a given data migration policy, and then optimize the data migration process using the selected postscaling encoding matrix. Our scaling scheme requires the minimal data movement while achieving uniform data distribution. To validate the efficiency of our scheme, we implement it atop a networked file system. Extensive experiments show that our scaling scheme reduces 7.94% to 58.87%, and 39.52% on average, of the scaling time over the basic scheme.