An Enterprise-Grade Open-Source Data Reduction Architecture for All-Flash Storage Systems

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.

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

“…School (AFS) algorithm to obtain the optimal initial parameters of the LMBPNN model, to improve the LMBP performance by obtaining initial the parameter efficiency [23] . Although there are too many iterations in AFS, it easily falls into the local extremum, which affects its optimization performance [24] . Therefore, this study proposes a strategy of introducing the attenuation factor and cut-off factor to improve it and to accelerate algorithm's convergence speed, as shown in Formula (9).…”