Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices upon the traditional storage system rely on the paging mechanism to migrate pages to the host DRAM, tending to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, an efficient fine-grained read framework, Pipette, is proposed in this article as an extension to the traditional I/O framework. With adaptive design for caching, merging, and scheduling, Pipette explores locality and acceleration for fine-grained read requests to establish an efficient byte-granular read path upon the dedicated byte-addressable interface. When the Pipette prototype on an SSD runs popular workloads, we measured throughput gains by up to 50% and 54% with traffic reduction in the range of$41.3\times $and$56.5\times $.

Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices tend to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, a fine-grained read framework, Pipette, is proposed in this paper, as an extension to the traditional I/O framework. With an adaptive caching design, Pipette framework offers a tremendous reduction in I/O traffic as well as achieves significant performance gain. A Pipette prototype was implemented with Ext4 file system on an SSD for two real-world applications, where the I/O throughput is improved by 31.6% and 33.5%, and the I/O traffic is reduced by 95.6% and 93.6%, respectively.

To meet the strict service level agreement requirements of recommendation systems, the entire set of embeddings in recommendation systems needs to be loaded into the memory. However, as the model and dataset for production-scale recommendation systems scale up, the size of the embeddings is approaching the limit of memory capacity. Limited physical memory constrains the algorithms that can be trained and deployed, posing a severe challenge for deploying advanced recommendation systems. Recent studies offload the embedding lookups into SSDs, which targets the embedding-dominated recommendation models. This paper takes it one step further and proposes to offload the entire recommendation system into SSD with in-storage computing capability. The proposed SSD-side FPGA solution leverages a low-end FPGA to speed up both the embedding-dominated and MLP-dominated models with high resource efficiency. We evaluate the performance of the proposed solution with a prototype SSD. Results show that we can achieve 20-100× throughput improvement compared with the baseline SSD and 1.5-15× improvement compared with the state-of-art.

Byte-addressable, non-volatile memory (NVM) technologies enable fast persistent updates but incur potential data inconsistency upon a failure. Recent proposals present several persistency models to guarantee data consistency. However, they fail to express the minimal persist ordering as a result of inducing unnecessary ordering constraints. In this paper, we propose i-BEP, a non-redundant high concurrency memory persistency model, which expresses epoch dependency via persist directed acyclic graph instead of program order. Additionally, we propose two techniques, background persist and deferred eviction, to enhance the performance of i-BEP. We demonstrate that i-BEP can improve the performance by 15% for typical data structures on average over buffered epoch persistency (BEP) model.

Emerging persistent memories (PMs) provide both byte addressability as DRAM and persistency as conventional storage technologies. Recent work on persistent memory file systems, such as BPFS, PMFS, have gained better performance by leveraging the dual characteristics. However, we observe that persistent memory file systems experience dramatic performance degradation over a long run. This phenomenon is referred to as file system aging. We find that the performance degradation is attributed to the inefficiency of storage management for both file space and dentry space. We also find that persistent memories wear out more quickly as file system ages. To address such issues, we propose SanGuo, a novel scatter-gather storage management mechanism for aged file systems on persistent memory. SanGuo consists of two key techniques. First, Scatter-alloc maximizes the efficiency and performance of file allocation while providing wear-leveling. Second, Gather-free accelerates the dentry operations, including dentry allocation, lookup and reclaim, especially for a directory file containing a large number of dentries. Experimental results show that SanGuo performs better wear-leveling while providing significant performance speed up (e.g., up to 10.33×, 8.9× respectively for Webproxy and Varmail workloads).