Abstract
Data reduction technologies have proven their effectiveness to decrease the ever-growing demands on storage system capacities, but also introduce new complexity in the system I/O stack that can easily invalidate well-known best practices. In this paper, we conduct an extensive set of experiments on an enterprise all-flash storage (AFS) system equipped with an open-source data reduction module, i.e., RedHat VDO, and reveal novel observations on the performance gap between the state-of-the-art and the optimal AFS stack with integrated data reduction. We then offer cross-layer optimizations to enhance the performance of AFS, which range from deriving new optimal hardware RAID configurations up to modifications of the enterprise storage stack tailored to the major bottlenecks observed. By implementing all proposed optimizations in an enterprise AFS, we show up to 12.5x speedup over the baseline AFS with integrated data reduction, and up to 57x performance/cost improvement over an optimized AFS (with no data reduction) for 100% write, high-reduction workload scenarios.
Published Version
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