Abstract
By leveraging data reduction technologies, such as data compression, all flash-based storage systems can have the same total cost of ownership (TCO) as traditional HDD-based storage systems. Thus, data compression has become a commodity feature for space efficiency and reliability in flash-based storage systems by reducing write traffic and space capacity demand. However, it introduces noticeable processing overheads on the critical I/O path, which degrades the system performance significantly. Existing data compression schemes for flash-based storage systems use fixed compression algorithms for all the incoming write data, failing to recognize and exploit the significant diversity in compressibility and access patterns of data and missing an opportunity to improve the system performance, the space efficiency or both. To achieve a reasonable trade-off between these two important design objectives, in this paper we introduce an Elastic Data Compression scheme, called EDC, which exploits the data compressibility and access intensity characteristics by judiciously matching data of different compressibility with different compression algorithms while leveraging the access idleness. Specifically, for compressible data blocks EDC exploits the compression diversity of the workload, and employs algorithms of higher compression rate in periods of lower system utilization and algorithms of lower compression rate in periods of higher system utilization. For non-compressible (or very lowly compressible) data blocks, it will write them through to the flash storage directly without any compression. The experiments conducted on our lightweight prototype implementation of the EDC system show that EDC saves storage space by up to 38.7 percent, with an average of 33.7 percent . In addition, it significantly outperforms the fixed compression schemes in the I/O performance measure by up to 61.4 percent, with an average of 36.7 percent.
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More From: IEEE Transactions on Parallel and Distributed Systems
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