We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion techniques, DiTTO enables the synthesis of high-fidelity continuous traces that capture temporal dynamics and inter-device dependencies with user-defined configurations. Our experimental results demonstrate that DiTTO can generate traces with high fidelity and diversity while aligning closely with guided configurations with only 8% errors.

Thanks to the advance of device scaling technologies, the capacity of SSDs is rapidly increasing. Such increase, however, comes at the cost of a huge index table requiring large DRAM. To provide reasonable performance with less DRAM, various index structures exploiting locality and regularity of I/O references have been proposed. However, they provide deteriorated performance depending on I/O patterns and storage fragmentation. This paper proposes a novel approximate index structure, called AppL, which combines memory-efficient approximate indices and an LSM-tree that has an append-only and sorted nature. AppL reduces the index size to 6-8-bits per entry, which is considerably smaller than the typical index structures requiring 32-64-bits, and maintains such high memory efficiency irrespective of locality and fragmentation. By alleviating memory pressure, AppL achieves 33.6-72.4% shorter read latency and 28.4%-83.4% higher I/O throughput than state-of-the-art techniques.

Android, with its vast global adoption and diverse hardware ecosystem, poses unique challenges for performance benchmarking, particularly from a user-centric perspective. Traditional benchmarks often fail to capture the intricacies of userperceived performance, relying on component-level metrics or synthetic workloads that do not reflect real-world usage. This paper proposes Real-Time User-Experience, RTUX, a novel benchmarking tool designed to measure Android system performance as perceived by users. RTUX employs external camera-based GUI state recognition and scenario-based testing to evaluate app loadtimes and in-app transitions under diverse conditions. Using CNN models and a unique system structure, RTUX reliably replays human-like interactions, enabling repeatable and robust performance assessments. Through experiments with 100 scenario repetitions involving popular Android apps, we uncover some system bottlenecks, such as suboptimal writeback configurations and I/O scheduler inefficiencies. The tool demonstrates how targeted optimizations can yield tangible improvements in user experience.

We present BigKV, a key-value cache specifically designed for caching large objects in an all-flash array (AFA). The design of BigKV is centered around the unique property of a cache: since it contains a copy of the data, exact bookkeeping of what is in the cache is not critical for correctness. By ignoring hash collisions, approximating metadata information, and allowing data loss from failures, BigKV significantly increases the cache hit ratio and keeps more useful objects in the system. Experiments on a real AFA show that our design increases the throughput by 3.1× on average and reduces the average and tail latency by 57% and 81%, respectively.

We propose a new I/O architecture for NAND flash-based SSDs, called application-managed flash (AMF) and present two case studies to show its usefulness. In a typical SSD controller, an intermediate software layer, called the flash translation layer (FTL), is employed between NAND flash chips and a host interface. The main responsibility of an FTL is to provide interoperability with conventional HDDs, but this interoperability comes at the cost of extra hardware resources and degraded I/O performance. The proposed AMF refactors the flash storage architecture so that an SSD controller exposes append-only segments, which do not permit overwriting. This refactoring dramatically improves performance of applications and reduces hardware costs by allowing applications to directly manage flash storage with minimal supports from the SSD controller. In order to understand the benefits of AMF, we study two popular applications: a log-structured file system (F2FS) and a key-value store (RocksDB). Our experiments show that the DRAM in the flash controller is reduced by 128X and the performances of the file system and the key-value store improve by 80 and 54 percent, respectively, over conventional SSDs.

We propose LightStore, a key-value flash store, as a substitute for x86-based storage servers. A LightStore node has a low-power embedded-class processor, a few gigabytes of DRAM and a few terabytes of NAND flash, and can be directly connected to a network port in a datacenter. A large-scale distributed storage cluster can be formed simply by adding more LightStore nodes to the network. Applications in a datacenter can take multiple software-defined views of LightStore stores via thin LightStore adapter layers, which translate conventional KV, YCSB, block, and file accesses to KV ones for LightStore. LightStore is estimated to be 2.0x power-efficient and 2.3x space-efficient than an x86-based all-flash array system of the same capacity. Experimental results on our LightStore prototype show that 1) the LightStore node performance is comparable to an x86 server with a single SSD; 2) a four-node LightStore cluster exhibits up to 7.4x better ops/J than an x86 server with four SSDs.