Intelligent clustering guided adaptive prefetching and buffer management for stream processing

Real-time stream data processing is required to handle big data with low latency to process a large amount of incessant streaming data. We propose a clustering based intel-igent prefetching scheme and its associated DRAM-PCM (phase change memory) hybrid memory management policy especially for stream processing. To alleviate stream processing's burden of requests accessing main memory, an intelligence prefetching technique is designed to reflect stream processing behavior to reduce the amount of memory access by improving buffer hit ratio. Because stream processing has to guarantee relatively small latency to the users, it is especially important for stream processsing to process the stream data in strict time. By using a hybrid memory structure, we take such advantages like high performance, low energy consumption, and memory scalability. And by using clustering based smart prefetching, we could improve buffer hit rate and because of this, overall system performance can be enhanced. Our proposed architecture and clustering based prefetching method can improve system performance by 1.15 times, compared with hybrid memory and buffer architecture without any prefetching scheme of conventional model and also energy consumption by 1.23 times, compared with DRAM only conventional model.