Patrick P. C. Lee

Qingxiu Liu, Jiazhen Cai, Siyuan Sheng et al.

Fast and scalable metadata management across multiple metadata servers is crucial for distributed file systems to handle numerous files and directories. Client-side caching of frequently accessed metadata can mitigate server loads, but incurs significant overhead and complexity in maintaining cache consistency when the number of clients increases. We explore caching in programmable switches by serving file-system metadata requests from multiple clients on the switch data plane. Despite prior efforts on in-switch key-value caching, they fail to address the path dependencies specific to file-system semantics. We propose Fletch, an in-switch file-system metadata caching framework that leverages programmable switches to serve file-system metadata requests from multiple clients directly in the switch data plane. Unlike prior in-switch key-value caching approaches, Fletch addresses file-system-specific path dependencies under stringent switch resource constraints. We implement Fletch atop Hadoop HDFS and evaluate it on a Tofino-switch testbed using real-world file-system metadata workloads. Fletch achieves up to 181.6% higher throughput than vanilla HDFS and complements client-side caching with throughput gains of up to 139.6%. It also incurs low latencies and limited switch resource usage.

Shuting Qiu, Fang Dong, Siyu Tan et al.

Mobile edge computing (MEC) can pre-cache deep neural networks (DNNs) near end-users, providing low-latency services and improving users' quality of experience (QoE). However, caching all DNN models at edge servers with limited capacity is difficult, and the impact of model loading time on QoE remains underexplored. Hence, we introduce dynamic DNNs in edge scenarios, disassembling a complete DNN model into interrelated submodels for more fine-grained and flexible model caching and request routing solutions. This raises the pressing issue of jointly deciding request routing and submodel caching for dynamic DNNs to balance model inference precision and loading latency for QoE optimization. In this paper, we study the joint dynamic model caching and request routing problem in MEC networks, aiming to maximize user request inference precision under constraints of server resources, latency, and model loading time. To tackle this problem, we propose CoCaR, an offline algorithm based on linear programming and random rounding that leverages dynamic DNNs to optimize caching and routing schemes, achieving near-optimal performance. Furthermore, we develop an online variant of CoCaR, named CoCaR-OL, enabling effective adaptation to dynamic and unpredictable online request patterns. The simulation results demonstrate that the proposed CoCaR improves the average inference precision of user requests by 46% compared to state-of-the-art baselines. In addition, in online scenarios, CoCaR-OL achieves an improvement of no less than 32.3% in user QoE over competitive baselines.

Juncheng Zhang, Yuanming Ren, Yongkun Li et al.

Disk-based graph indexes for approximate nearest neighbor search (ANNS) must serve latency-sensitive queries and throughput-demanding updates concurrently. We observe that over 40% of search-thread CPU time is spent stalling on disk I/O; such idle cycles are invisible to thread-level scheduling yet available for other work. We present LIOS(Leverage I/O Stall), a framework that executes index updates inside search-side I/O stall windows. LIOS introduces three techniques: (i) splitting each update into resumable subtasks small enough to fit within a single stall window; (ii) bounding the expected overrun of update subtasks to a given threshold; and (iii) dynamically adjusting the fraction of idle time devoted to updates to drive end-to-end search latency degradation toward a user-specified target. We integrate LIOS into two update-optimized ANNS systems, FreshDiskANN and OdinANN. LIOS achieves speedups of up to 2.68$\times$ in insertion and 2.18$\times$ in deletion, with search latency degradation maintained near the user-specified target.

Shujie Han, Feng Jiang, Patrick P. C. Lee et al.

Large Language Model (LLM) training is frequently interrupted by a heterogeneous spectrum of failures, from common GPU crashes to catastrophic cluster-wide outages. Existing checkpointing systems rely on monolithic, single-tier storage backend, forcing a trade-off between state-saving overhead and recovery speed. We propose TierCheck, a cluster-aware tiered checkpointing system that aligns storage placement with failure heterogeneity. TierCheck adopts a three-tier design that maintains lightweight differential checkpoints in local and peer memory for fast localized recovery, while asynchronously migrating heavyweight base checkpoints to remote persistent storage. It also ensures strict global consistency across tiers without stalling training, and achieves fast cluster-aware checkpoint restoration during recovery. Evaluations on models up to 40 billion parameters show that TierCheck achieves low training overhead, reduces end-to-end checkpointing time to under 10s, and supports high-frequency checkpointing, ultimately striking an optimal balance between low-overhead persistence and fast recovery.

Qingxiu Liu, Cyril Y. He, Hanser Jiang et al.

Mixture-of-Experts (MoE) models have become a dominant paradigm for scaling large language models, but their rapidly growing parameter sizes introduce a fundamental inefficiency during inference: most expert weights remain idle in GPU memory while competing with performance-critical runtime state such as the key-value (KV) cache. Since KV cache capacity directly determines serving throughput, this mismatch leads to underutilized memory and degraded performance. In this paper, we present FluxMoE, a new MoE inference system that decouples expert parameters from persistent GPU residency. FluxMoE introduces an expert paging abstraction that treats expert weights as streamed, transient resources, materializing them on demand and evicting them immediately after use, allowing GPU memory to be preferentially allocated to throughput-critical runtime state. We implement FluxMoE atop vLLM to enable efficient MoE inference under severe memory constraints. Experimental results demonstrate that FluxMoE achieves up to 3.0$\times$ throughput gains over vLLM in memory-intensive regimes, without compromising model fidelity.

Yuanming Ren, Juncheng Zhang, Yanjing Ren et al.

Managing large-scale vector datasets with disk-based approximate nearest neighbor search (ANNS) systems faces critical efficiency challenges stemming from the co-location of vector data and auxiliary index metadata. Our analysis of state-of-the-art ANNS systems reveals that such co-location incurs substantial storage overhead, generates excessive reads during search queries, and causes severe write amplification during updates. We present DecoupleVS, a decoupled vector storage management framework that enables specialized optimizations for vector data and auxiliary index metadata. DecoupleVS incorporates various design techniques for effective compression, data layouts, search queries, and updates, so as to significantly reduce storage space, while maintaining high search and update performance and high search accuracy. Evaluation on real-world public and proprietary billion-scale datasets shows that DecoupleVS reduces storage space by up to 58.7\%, while delivering competitive or improved search query and update performance, compared to state-of-the-art monolithic disk-based ANNS systems.

Cheng He, Xijie Liang, Zengrong Zheng et al.

Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers. They often encode time series data in a black-box manner and rely on trial-and-error optimization solely based on forecasting performance, leading to limited interpretability and theoretical understanding. Furthermore, the dynamics in data distribution over time and frequency domains pose a critical challenge to accurate forecasting. We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series, so as to achieve interpretable and robust time series forecasting. FIRE introduces several key innovations: (i) independent modeling of amplitude and phase components, (ii) adaptive learning of weights of frequency basis components, (iii) a targeted loss function, and (iv) a novel training paradigm for sparse data. Extensive experiments demonstrate that FIRE consistently outperforms state-of-the-art models on long-term forecasting benchmarks, achieving superior predictive performance and significantly enhancing interpretability of time series

Shujie Han, Jun Wu, Erci Xu et al.

To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e.g., Backblaze SMART logs). However, in real-world production environments, the data quality is imperfect (e.g., inaccurate labeling, missing data samples, and complex failure types), thereby degrading the prediction accuracy. We present RODMAN, a robust data preprocessing pipeline that refines data samples before feeding them into ML models. We start with a large-scale trace-driven study of over three million disks from Alibaba Cloud's data centers, and motivate the practical challenges in ML-based disk failure prediction. We then design RODMAN with three data preprocessing echniques, namely failure-type filtering, spline-based data filling, and automated pre-failure backtracking, that are applicable for general ML models. Evaluation on both the Alibaba and Backblaze datasets shows that RODMAN improves the prediction accuracy compared to without data preprocessing under various settings.

Jingwei Li, Patrick P. C. Lee, Chufeng Tan et al.

Encrypted deduplication combines encryption and deduplication to simultaneously achieve both data security and storage efficiency. State-of-the-art encrypted deduplication systems mainly build on deterministic encryption to preserve deduplication effectiveness. However, such deterministic encryption reveals the underlying frequency distribution of the original plaintext chunks. This allows an adversary to launch frequency analysis against the ciphertext chunks and infer the content of the original plaintext chunks. In this paper, we study how frequency analysis affects information leakage in encrypted deduplication storage, from both attack and defense perspectives. Specifically, we target backup workloads, and propose a new inference attack that exploits chunk locality to increase the coverage of inferred chunks. We further combine the new inference attack with the knowledge of chunk sizes and show its attack effectiveness against variable-size chunks. We conduct trace-driven evaluation on both real-world and synthetic datasets and show that our proposed attacks infer a significant fraction of plaintext chunks under backup workloads. To defend against frequency analysis, we present two defense approaches, namely MinHash encryption and scrambling. Our trace-driven evaluation shows that our combined MinHash encryption and scrambling scheme effectively mitigates the severity of the inference attacks, while maintaining high storage efficiency and incurring limited metadata access overhead.

Chuan Qin, Jingwei Li, Patrick P. C. Lee

Rekeying refers to an operation of replacing an existing key with a new key for encryption. It renews security protection, so as to protect against key compromise and enable dynamic access control in cryptographic storage. However, it is non-trivial to realize efficient rekeying in encrypted deduplication storage systems, which use deterministic content-derived encryption keys to allow deduplication on ciphertexts. We design and implement REED, a rekeying-aware encrypted deduplication storage system. REED builds on a deterministic version of all-or-nothing transform (AONT), such that it enables secure and lightweight rekeying, while preserving the deduplication capability. We propose two REED encryption schemes that trade between performance and security, and extend REED for dynamic access control. We implement a REED prototype with various performance optimization techniques and demonstrate how we can exploit similarity to mitigate key generation overhead. Our trace-driven testbed evaluation shows that our REED prototype maintains high performance and storage efficiency.

Mingqiang Li, Chuan Qin, Patrick P. C. Lee

We present CDStore, which disperses users' backup data across multiple clouds and provides a unified multi-cloud storage solution with reliability, security, and cost-efficiency guarantees. CDStore builds on an augmented secret sharing scheme called convergent dispersal, which supports deduplication by using deterministic content-derived hashes as inputs to secret sharing. We present the design of CDStore, and in particular, describe how it combines convergent dispersal with two-stage deduplication to achieve both bandwidth and storage savings and be robust against side-channel attacks. We evaluate the performance of our CDStore prototype using real-world workloads on LAN and commercial cloud testbeds. Our cost analysis also demonstrates that CDStore achieves a monetary cost saving of 70% over a baseline cloud storage solution using state-of-the-art secret sharing.