A hybrid memory built by SSD and DRAM to support in-memory Big Data analytics

Big Data requires a shift in traditional computing architecture. The in-memory computing is a new paradigm for Big Data analytics. However, DRAM-based main memory is neither cost-effective nor energy-effective. This work combines flash-based solid state drive (SSD) and DRAM together to build a hybrid memory, which meets both of the two requirements. As the latency of SSD is much higher than that of DRAM, the hybrid architecture should guarantee that most requests are served by DRAM rather than by SSD. Accordingly, we take two measures to enhance the hit ratio of DRAM. First, the hybrid memory employs an adaptive prefetching mechanism to guarantee that data have already been prepared in DRAM before they are demanded. Second, the DRAM employs a novel replacement policy to give higher priority to replace data that are easy to be prefetched because these data can be served by prefetching once they are demanded once again. On the contrary, the data that are hard to be prefetched are protected by DRAM. The prefetching mechanism and replacement policy employed by the hybrid memory rely on access patterns of files. So, we propose a novel pattern recognition method by improving the LZ data compression algorithm to detect access patterns. We evaluate our proposals via prototype and trace-driven simulations. Experimental results demonstrate that the hybrid memory is able to extend the DRAM by more than twice.