Byte-addressable, non-volatile memory (NVRAM) combines the benefits of DRAM and flash memory. However, due to its slower speed than DRAM, it is best to deploy it in combination with typical DRAM. In such Hybrid NVRAM systems, frequently accessed, hotpages can be stored in DRAM while other cold pages can reside in NVRAM, providing the benefits of both high performance (from DRAM) and lower power consumption and cost/performance (from NVRAM). While the idea seems beneficial, realizing an efficient hybrid NVRAM system requires careful page migration and accurate data temperature measurement. Existing solutions, however, often cause invalid migrations due to inaccurate data temperature accounting, because hot and cold pages are separately identified in DRAM and NVRAM regions. Moreover, since a new NVRAM frame is always allocated for each page swapped back NVRAM, a large amount of unnecessary NVRAM writes are generated during each page migration. Based on these observations, we propose APMigrate, an adaptive data migration approach for hybrid NVRAM systems. APMigrate consist of two parts, UIMigrate and LazyWriteback. UIMigrate focuses on eliminating invalid page migrations by considering data temperature in the entire DRAM-NVRAM space, while LazyWriteback focus on rewriting only dirty data back when the page is swapped back to NVRAM. Our experiments using SPEC 2006 show that APMigrate can reduce the number of migrations and improves performance by up to 90 percent compared to existing state-of-the-art approaches. For some workloads, LazyWriteback can reduce unnecessary NVRAM writes for existing page migrations by up to 75 percent.

Byte-addressable, non-volatile memory (NVRAM) combines the benefits of DRAM and flash memory. Its slower speed compared to DRAM, however, makes it hard to entirely replace DRAM with NVRAM. Hybrid NVRAM systems that equip both DRAM and NVRAM on the memory bus become a better solution: frequently accessed, hot pages can be stored in DRAM while other cold pages can reside in NVRAM. This way, the system gets the benefits of both high performance (from DRAM) and lower power consumption and cost/performance (from NVRAM). Realizing an efficient hybrid NVRAM system requires careful page migration and accurate data temperature measurement. Existing solutions, however, often cause invalid migrations due to inaccurate data temperature accounting, because hot and cold pages are separately identified in DRAM and NVRAM regions.Based on this observation, we propose UIMigrate, an adaptive data migration approach for hybrid NVRAM systems. The key idea is to consider data temperature across the whole DRAM-NVRAM space when determining whether a page should be migrated between DRAM and NVRAM. In addition, UIMigrate adapts workload changes by dynamically adjusting migration decisions as workload changes. Our experiments using SPEC 2006 show that UIMigrate can reduce the number of migrations and improves performance by up to 90.4% compared to existing state-of-the-art approaches.

In deduplication-based backup systems, the removal of redundant data transforms the otherwise logically adjacent data chunks into physically scattered chunks on the disks. This, in effect, changes the retrieval operations from sequential to random and significantly degrades the performance of restoring data. These scattered chunks are called fragmented data and many techniques have been proposed to identify and sequentially rewrite such fragmented data to new address areas, trading off the increased storage space for reduced number of random reads (disk seeks) to improve the restore performance. However, existing solutions for backup workloads share a common assumption that every read operation involves a large fixed-size window of contiguous chunks, which restricts the fragment identification to a fixed-size read window. This can lead to inaccurate identifications due to false positives since the data fragments can vary in size and appear in any different and unpredictable address locations. Based on these observations, we propose FGdefrag , a Fine-Grained defragmentation approach that uses variable-sized and adaptively located data groups, instead of using fixed-size read windows, to accurately identify and effectively remove fragmented data. When we compare its performance to those of existing solutions, FGdefrag not only reduces the amount of rewritten data but also significantly improves the restore performance. Our experimental results show that FGdefrag can improve the restore performance by 14 to 329 percent, while simultaneously reducing the rewritten data by 25 to 87 percent.