Hybrid storage techniques are useful methods to improve the cost performance for input-output (IO) intensive workloads. These techniques choose areas of concentrated IO accesses and migrate them to an upper tier to extract as much performance as possible through greater use of upper tier areas. Automated tiered storage with fast memory and slow flash storage (ATSMF) is a hybrid storage system situated between non-volatile memories (NVMs) and solid-state drives (SSDs). ATSMF aims to reduce the average response time for IO accesses by migrating areas of concentrated IO access from an SSD to an NVM. When a concentrated IO access finishes, the system migrates these areas from the NVM back to the SSD. Unfortunately, the published ATSMF implementation temporarily consumes much NVM capacity upon migrating concentrated IO access areas to NVM, because its algorithm executes NVM migration with high priority. As a result, it often delays evicting areas in which IO concentrations have ended to the SSD. Therefore, to reduce the consumption of NVM while maintaining the average response time, we developed new techniques for making ATSMF more practical. The first is a queue handling technique based on the number of IO accesses for NVM migration and eviction. The second is an eviction method that selects only write-accessed partial regions in finished areas. The third is a technique for variable eviction timing to balance the NVM consumption and average response time. Experimental results indicate that the average response times of the proposed ATSMF are almost the same as those of the published ATSMF, while the NVM consumption is three times lower in best case.

In recent years, the need to build solid state drive (SSD)-based cloud storage systems has been increasing in order to process the big data generated by lots of Internet of Things devices and Internet users. Because these kinds of cloud systems require high performance and reliable storage, the use of flash-based Redundant Array of Independent Disks (RAID) will increase. But in flash-based RAID storage, parity data must be updated with every data write operation, which can more quickly overwhelm SSD's lifespan. To solve this problem, this letter proposes parity data deduplication for OpenStack cloud storage systems using an all flash array. Unlike the traditional data deduplication method, it only removes parity data, which will be stored in the parity disks of the all flash array. Experiments show that the proposed parity data deduplication method can efficiently reduce the number of parity data write operations, compared to the traditional data deduplication method.

The Log-structured File System (LFS) transforms random writes to a huge sequential one to provide superior write performance on storage devices. However, LFS inherently suffers from overhead incurred by cleaning segments. Specifically, when file system utilization is high and the system is busy, write performance of LFS degenerates significantly due to high cleaning cost. Also, in the newer flash memory based SSD storage devices, cleaning leads to reduced SSD lifetime as it incurs more writes. In this paper, we propose an enhancement to the original LFS to alleviate the performance degeneration due to cleaning when the system is busy. The new scheme, which we call Slack Space Recycling (SSR), allows LFS to delay on-demand cleaning during busy hours such that cleaning may be done when the load is much lighter. Specifically, it writes modified data directly to invalid areas (slack space) of used segments instead of cleaning on-demand, pushing back cleaning for later. SSR also has the added benefit of increasing the lifetime of the now popular SSD storage devices. We implement the new SSR-LFS file system in Linux and perform a large set of experiments. The results of these experiments show that the SSR scheme significantly improves performance of LFS for a wide range of storage utilization settings and that the lifetime of SSDs is extended considerably.