NVMe SSD hugely boosts the I/O speed, with up to GB/s throughput and microsecond-level latency. Unfortunately, DBMS users can often find their high-performanced storage devices tend to deliver less-than-expected or even worse performance when compared to their traditional peers. While many works focus on proposing new DBMS designs to fully exploit NVMe SSDs, few systematically study the symptoms, root causes and possible detection methods of such performance mismatches on existing databases. In this paper, we start with an empirical study where we systematically expose and analyze the performance mismatches on six popular databases via controlled configuration tuning. From the study, we find that all six databases can suffer from performance mismatches. Moreover, we conclude that the root causes can be categorized as databases' unawareness of new storage devices characteristics in I/O size, I/O parallelism and I/O sequentiality. We report 17 mismatches to developers and 15 are confirmed. Additionally, we realize testing all configuration knobs yields low efficiency. Therefore, we propose a fast performance mismatch detection framework and evaluation shows that our framework brings two orders of magnitude speedup than baseline without sacrificing effectiveness.

Images occupy a large amount of storage in data centers. To cope with the explosive growth of the image storage requirement, image compression techniques are devised to shrink the size of every single image at first. Furthermore, image deduplication methods are proposed to reduce the storage cost as they could be used to eliminate redundancy among images. However, state-of-the-art image deduplication methods either can only eliminate file-level coarse-grained redundancy or cannot guarantee lossless deduplication. In this work, we propose a new lossless image deduplication framework to eliminate fine-grained redundancy among images. It first decodes images to expose similarity, then eliminates fine-grained redundancy on the decoded data by delta compres-sion, and finally re-compresses the remaining data by image compression encoding. Based on this framework, we propose a novel lossless similarity-based deduplication (SBD) scheme for decoded image data (called imDedup). Specifically, imDedup uses a novel and fast sampling method (called Feature Map) to detect similar images in a two-dimensional way, which greatly reduces computation overhead. Meanwhile, it uses a novel delta encoder (called Idelta) which incorporates image compression encoding characteristics into deduplication to guarantee the remaining deduplicated image data to be friendly re-compressed via image encoding, which significantly improves the compression ratio. We implement a prototype of imDedup for JPEG images, and demonstrate its superiority on four datasets: Compared with exact image deduplication, imDedup achieves a 19%-38% higher compression ratio by efficiently eliminating fine-grained redundancy. Compared with the similarity detector and delta encoder of state-of-the-art SBD schemes running on the decoded image data, imDedup achieves a 1.8×-3.4× higher throughput and a 1.3 ×-1. 6 × higher compression ratio, respectively.