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Mai Zheng

Publications and source records attributed to Mai Zheng.

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HORIZON: A Read-Efficient Firmware for DNA Storage with Horizontal Layout

DNA storage is a promising medium for long-term archiving, but its read performance is limited by coarse-grained random access. Existing random-access DNA storage designs suffer from high read amplification because their sequential layouts co-locate frequently and infrequently accessed data under the same primer pair, where any read must retrieve all associated strands even when only a small fraction is needed. We present HORIZON, a read-efficient allocation policy for DNA block devices that reduces read amplification through activity-aware horizontal placement. HORIZON first introduces a horizontal layout distributing writes round-robin across primer pairs, rather than filling each sequentially. It classifies newly written blocks in the write buffer as active or inactive, tracks recent primer-pair accesses using a sliding-window temperature model, and allocates blocks based on block activity and primer-pair occupancy. Simulations show HORIZON consistently reduces read amplification compared with state-of-the-art schemes across MSR and FIU traces and synthetic filesystem workloads.

cs.ET

InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk

Managing modern computing infrastructure has become a steadily harder problem due to the ever-increasing complexity. Recent advances in AI agents create a timely opportunity to automate infrastructure management tasks, but it remains unclear how well such agents can handle real-world infrastructure complexity. We present InfraBench, a benchmark suite for evaluating AI agents on realistic infrastructure tasks across the full system stack and full operational lifecycle with fine-grained risk assessment. Experiments with 15 agent-model configurations show that even the strongest agent cannot secure a full score across all tasks. Mean effective scores range from roughly 40% to 88% (with per-configuration standard errors of 6-12 points), repeating every task three times reveals that top configurations still pass only a fraction of their attempts, and per-check scoring exposes a general failure pattern: agents may routinely satisfy short-term objectives while leaving non-durable changes, broken distributed invariants, unsafe side effects, and uncleaned state behind. INFRABENCH, including its live leaderboard, tasks, and evaluation harness, is publicly available at infraben.ch.

cs.AI

Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems

AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of 290 public reports. Our study reveals two fundamental gaps: users and agents have limited information about filesystem effects and insufficient control over them. To close these gaps, we propose to shift information and control from agents to filesystems. We introduce agent-native filesystems and identify three primitives they should provide: introspect effects, undo mutations, and gate accesses. These primitives let agents operate autonomously while reserving user interaction for sensitive accesses and final review. We build YoloFS, an agent-native filesystem. YoloFS stages mutations until the user commits them, snapshots intermediate states for agent self-correction, and uses progressive permission to let users adapt access rules during execution. We evaluate YoloFS with a new methodology that captures interactions among the user, agent, and filesystem. On 11 tasks with hidden side effects, YoloFS enables agents to self-correct in 8 and stages all mutations for user review. On 112 routine tasks, YoloFS reduces user interaction while matching the baseline success rate. YoloFS is open-sourced at https://github.com/YoloFS/YoloFS.

cs.OS

StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems

Automatically configuring storage systems is hard: parameter spaces are large and conditions vary across workloads, deployments, and versions. Heuristic and ML tuners are often system specific, require manual glue, and degrade under changes. Recent LLM-based approaches help but usually treat tuning as a single-shot, system-specific task, which limits cross-system reuse, constrains exploration, and weakens validation. We present StorageXTuner, an LLM agent-driven auto-tuning framework for heterogeneous storage engines. StorageXTuner separates concerns across four agents - Executor (sandboxed benchmarking), Extractor (performance digest), Searcher (insight-guided configuration exploration), and Reflector (insight generation and management). The design couples an insight-driven tree search with layered memory that promotes empirically validated insights and employs lightweight checkers to guard against unsafe actions. We implement a prototype and evaluate it on RocksDB, LevelDB, CacheLib, and MySQL InnoDB with YCSB, MixGraph, and TPC-H/C. Relative to out-of-the-box settings and to ELMo-Tune, StorageXTuner reaches up to 575% and 111% higher throughput, reduces p99 latency by as much as 88% and 56%, and converges with fewer trials.

cs.DB

On Fault Tolerance of Data Storage Systems: A Holistic Perspective

Data storage systems serve as the foundation of digital society. The enormous data generated by people on a daily basis make the fault tolerance of data storage systems increasingly important. Unfortunately, modern storage systems consist of complicated hardware and software layers interacting with each other, which may contain latent bugs that elude extensive testing and lead to data corruption, system downtime, or even unrecoverable data loss in practice. In this chapter, we take a holistic view to introduce the typical architecture and major components of modern data storage systems (e.g., solid state drives, persistent memories, local file systems, and distributed storage management at scale). Next, we discuss a few representative bug detection and fault tolerance techniques across layers with a focus on issues that affect system recovery and data integrity. Finally, we conclude with open challenges and future work.

cs.DC

Revisiting Computational Storage for Data Integrity and Security

The idea of computational storage device (CSD) has come a long way since at least 1990s [1], [2]. By embedding computing resources within storage devices, CSDs could potentially offload computational tasks from CPUs and enable near-data processing (NDP), reducing data movements and/or energy consumption significantly. While the initial hard-disk-based CSDs suffer from severe limitations in terms of on-drive resources, programmability, etc., the storage market has witnessed the commercialization of solid-state-drive (SSD) based CSDs (e.g., Samsung SmartSSD [3], ScaleFlux CSDs [4]) recently, which has enabled CSD-based optimizations for avariety of application scenarios (e.g., [5], [6], [7]).

cs.DC

Analyzing Configuration Dependencies of File Systems

File systems play an essential role in modern society for managing precious data. To meet diverse needs, they often support many configuration parameters. Such flexibility comes at the price of additional complexity which can lead to subtle configuration-related issues. To address this challenge, we study the configuration-related issues of two major file systems (i.e., Ext4 and XFS) in depth, and identify a prevalent pattern called multilevel configuration dependencies. Based on the study, we build an extensible tool called ConfD to extract the dependencies automatically, and create a set of plugins to address different configuration-related issues. Our experiments on Ext4, XFS and a modern copy-on-write file system (i.e., ZFS) show that ConfD was able to extract 160 configuration dependencies for the file systems with a low false positive rate. Moreover, the dependency-guided plugins can identify various configuration issues (e.g., mishandling of configurations, regression test failures induced by valid configurations). In addition, we also explore the applicability of ConfD on a popular storage engine (i.e., WiredTiger). We hope that this comprehensive analysis of configuration dependencies of storage systems can shed light on addressing configuration-related challenges for the system community in general.

cs.OS

Design and Implementation of ARA Wireless Living Lab for Rural Broadband and Applications

Addressing the broadband gap between rural and urban regions requires rural-focused wireless research and innovation. In the meantime, rural regions provide rich, diverse use cases of advanced wireless, and they offer unique real-world settings for piloting applications that advance the frontiers of wireless systems (e.g., teleoperation of ground and aerial vehicles). To fill the broadband gap and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity terrestrial wireless x-haul and access platforms as well as low-earth-orbit (LEO) satellite communications platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems, and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment (UE), base stations (BS), edge, and cloud, including support for Bring Your Own Device (BYOD), ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as Open RAN, NextG, and agriculture applications.

cs.NI

PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments.

cs.DC

Understanding Persistent-Memory Related Issues in the Linux Kernel

Persistent memory (PM) technologies have inspired a wide range of PM-based system optimizations. However, building correct PM-based systems is difficult due to the unique characteristics of PM hardware. To better understand the challenges as well as the opportunities to address them, this paper presents a comprehensive study of PM-related issues in the Linux kernel. By analyzing 1,553 PM-related kernel patches in-depth and conducting experiments on reproducibility and tool extension, we derive multiple insights in terms of PM patch categories, PM bug patterns, consequences, fix strategies, triggering conditions, and remedy solutions. We hope our results could contribute to the development of robust PM-based storage systems

cs.OS

$\lambda$FS: A Scalable and Elastic Distributed File System Metadata Service using Serverless Functions

The metadata service (MDS) sits on the critical path for distributed file system (DFS) operations, and therefore it is key to the overall performance of a large-scale DFS. Common "serverful" MDS architectures, such as a single server or cluster of servers, have a significant shortcoming: either they are not scalable, or they make it difficult to achieve an optimal balance of performance, resource utilization, and cost. A modern MDS requires a novel architecture that addresses this shortcoming. To this end, we design and implement $\lambda$FS, an elastic, high-performance metadata service for large-scale DFSes. $\lambda$FS scales a DFS metadata cache elastically on a FaaS (Function-as-a-Service) platform and synthesizes a series of techniques to overcome the obstacles that are encountered when building large, stateful, and performance-sensitive applications on FaaS platforms. $\lambda$FS takes full advantage of the unique benefits offered by FaaS $\unicode{x2013}$ elastic scaling and massive parallelism $\unicode{x2013}$ to realize a highly-optimized metadata service capable of sustaining up to 4.13$\times$ higher throughput, 90.40% lower latency, 85.99% lower cost, 3.33$\times$ better performance-per-cost, and better resource utilization and efficiency than a state-of-the-art DFS for an industrial workload.

cs.DC

On Failure Diagnosis of the Storage Stack

Diagnosing storage system failures is challenging even for professionals. One example is the "When Solid State Drives Are Not That Solid" incident occurred at Algolia data center, where Samsung SSDs were mistakenly blamed for failures caused by a Linux kernel bug. With the system complexity keeps increasing, such obscure failures will likely occur more often. As one step to address the challenge, we present our on-going efforts called X-Ray. Different from traditional methods that focus on either the software or the hardware, X-Ray leverages virtualization to collects events across layers, and correlates them to generate a correlation tree. Moreover, by applying simple rules, X-Ray can highlight critical nodes automatically. Preliminary results based on 5 failure cases shows that X-Ray can effectively narrow down the search space for failures.

cs.OS

Nature of System Calls in CPU-centric Computing Paradigm

Modern operating systems are typically POSIX-compliant with major system calls specified decades ago. The next generation of non-volatile memory (NVM) technologies raise concerns about the efficiency of the traditional POSIX-based systems. As one step toward building high performance NVM systems, we explore the potential dependencies between system call performance and major hardware components (e.g., CPU, memory, storage) under typical user cases (e.g., software compilation, installation, web browser, office suite) in this paper. We build histograms for the most frequent and time-consuming system calls with the goal to understand the nature of distribution on different platforms. We find that there is a strong dependency between the system call performance and the CPU architecture. On the other hand, the type of persistent storage plays a less important role in affecting the performance.

cs.OS