Abstract
As big data has evolved over the past few years, a lack of storage space and I/O bandwidth has become one of the most important challenges to overcome. To mitigate these problems, data compression schemes reduce the amount of data to be stored and transmitted at the cost of additional CPU overhead. Many researchers have attempted to reduce the computational load imposed on the CPU by data compression using specialized hardware. However, space savings through data compression often comes from only a small portion of data. Therefore, compressing all data, regardless of data compressibility, can waste computational resources. Our work aims to decrease the cost of data compression by introducing a selective data compression scheme based on data compressibility prediction. The proposed compressibility prediction method provides more fine-grained selectivity for combinational compression. Additionally, our method reduces the amount of resources consumed by the compressibility predictor, enabling selective compression at a low cost. To verify the proposed scheme, we implemented a DEFLATE compression system on a field-programmable gate array platform. Experimental results demonstrate that the proposed scheme improves compression throughput by 34.15% with a negligible decrease in compression ratio.
Highlights
As mobile devices as well as information and communication technology have evolved, the amount of data to be stored and processed has increased explosively [1]–[4]
We propose a DEFLATE compression offloading engine using a selective data compression method based on data compressibility prediction
We described an input DB analysis method for data compressibility prediction
Summary
As mobile devices as well as information and communication technology have evolved, the amount of data to be stored and processed has increased explosively [1]–[4]. This is because most systems cannot handle other tasks while compression algorithms are running For this reason, there have been many attempts to reduce the computational load of data compression by utilizing customized hardware resources, such as field-programmable gate arrays (FPGAs) [34]–[41], graphics processing units, and application-specific integrated circuits [42], [43]. We propose a DEFLATE compression offloading engine using a selective data compression method based on data compressibility prediction. The proposed hardware cost reduction technique reuses the computation of existing compression algorithms in implementing compressibility prediction.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have