Correlation-Aware Stripe Organization for Efficient Writes in Erasure-Coded Storage: Algorithms and Evaluation.

Erasure coding has been extensively employed for data availability protection in production storage systems by maintaining a low degree of data redundancy. However, how to mitigate the parity update overhead of partial stripe writes in erasure-coded storage systems is still a critical concern. In this paper, we study this problem from two new perspectives: data correlation and stripe organization. We propose $\mathsf{CASO}$CASO, a correlation-aware stripe organization algorithm, which captures data correlation of a data access stream and uses the data correlation characteristics for stripe organization. It packs correlated data into a small number of stripes to reduce the incurred I/Os in partial stripe writes, and further organizes uncorrelated data into stripes to leverage the spatial locality in later access. We implement $\mathsf{CASO}$CASO over Reed-Solomon codes and Azure's Local Reconstruction Codes, and show via extensive trace-driven evaluation that $\mathsf{CASO}$CASO reduces up to 29.8 percent of parity updates and reduces the write time by up to 46.7 percent. [ABSTRACT FROM AUTHOR]