arXiv subjects

Huaicheng Li

Publications and source records attributed to Huaicheng Li.

9 recordsLinked to original sources

arXiv2026

A Priority-Aware Dual-Channel Feature Fusion Method for Urban Rail Service Traffic Classification

With the deep integration of 5G and IoT in urban rail transit, service traffic grows explosively and accurate classification of heterogeneous service flows is essential for safe and efficient railway operations. Urban rail communication systems are subject not only to operational disturbances such as equipment failures and maintenance interference, but also to complex transmission patterns characterized by the interleaving of multi-service traffic flows. Under these operating conditions, the widespread deployment of proprietary protocols and the high prevalence of encrypted traffic further diminish the applicability of conventional port-based and Deep Packet Inspection (DPI) classification methods. To address these challenges, we propose a priority-aware dual-channel feature fusion framework. Raw traffic bytes and statistical features are mapped into grayscale images and processed by a dual-branch architecture: a transfer learning-enhanced ResNet extracts fine-grained byte-level textures, while a lightweight CNN captures macroscopic statistical patterns. A channel attention mechanism dynamically recalibrates cross-modal features, and a novel Priority-Sensitive Loss (PSL) that integrates business-criticality awareness with class-balance weighting to maximize recall for safety-critical services. Evaluated on a real-world urban rail dataset using priority-weighted metrics, the method achieves 98.74\% accuracy and 99.24\% weighted recall, with recall of 99.94\% and 99.41\% on the two safety-critical services:Communication-Based Train Control(CBTC) and Emergency Radio Dispatch(ERD), providing a reliable classification foundation for priority-aware resource scheduling in urban rail communications. With only 0.18M parameters and 0.4--0.7 ms end-to-end latency, offering an excellent balance between high-precision classification, low inference latency, and edge-deployment feasibility.

eess.SY↗

arXiv2026

Programming In-Storage Computing with Located, Stateful Dataflow

In-storage computing (ISC) reduces host--storage data movement by executing computation inside computational storage devices (CSDs). For multi-stage applications, realizing these benefits requires coordinating data placement, I/O--compute overlap, and device-resident state across the workflow, yet existing interfaces lack a unified abstraction for these decisions. We present Epic, an NVMe-based ISC stack that provides this abstraction by capturing data residency and lifetime in the program: location types declare logical residency, dataflow derives lifetimes for intermediate values and operation state within an invocation, and a keep primitive extends selected state across invocations. These semantics expose the complete offloaded workflow as a located, stateful dataflow. A storage-aware compiler transforms this workflow, performs movement-aware logical mapping and fusion, and exposes I/O--compute overlap; a runtime completes the plan using execution-time information, asynchronously binding work to physical resources and managing device-resident state. Across 12 file-scanning, database, and machine learning workloads, Epic is 1.6$\times$ faster on average than the strongest of five prior ISC systems, while achieving 4.2$\times$ speedup on average and up to 16.1$\times$ over the corresponding host baselines, and reducing application-side code by up to 14$\times$ in our implementations.

cs.AR↗

arXiv2026

Variational Inference for Bird's Eye View Segmentation in Autonomous Driving

The bird's eye view (BEV) has emerged as a pivotal approach for environmental perception in autonomous driving, providing a unified spatial representation for vehicles. Nevertheless, despite BEV's significance in addressing the challenges inherent to autonomous driving, effectively fusing data from multiple camera sensors and operating in complex external driving environments remains a considerable challenge. To mitigate this issue, we recast the BEV segmentation problem within a variational inference framework. In this paper, we propose a novel transformer-based variational flow transformation network for BEV segmentation, denoted as TVB. Our architecture implicitly learns the mapping from multiple camera views to a unified canonical BEV map during training by exploiting posterior BEV supervision. TVB employs a conditional variational auto encoder (CVAE) as its backbone and produces multiple BEV map candidates. To augment the realism of the generated BEV maps, we integrate normalizing flows into the map generation process, enabling the construction of more complex and expressive probability distributions. Furthermore, we design a BEV-attention fusion (BAF) module that harnesses attention mechanisms to adaptively integrate the multiple candidate BEV maps. Experimental results, evaluated on both the nuScenes and OPV2Vdatasets, demonstrate that our proposed method achieves superior performance in multi-camera view BEV segmentation and lane environment perception.

cs.CV↗

arXiv2026

Aquifer: Hierarchical Memory Pooling with CXL and RDMA for MicroVM Snapshots

Memory stranding wastes 25-35% of installed DRAM in production cloud clusters. Memory pooling over CXL and RDMA offers a remedy, but neither technology alone suffices: CXL provides low-latency, load/store-transparent access limited to a pod, while RDMA provides cluster-wide reach at higher latency with software overhead. A hierarchical architecture combining both tiers is the practical path forward, yet remains unexplored for MicroVM-based serverless computing, where snapshot restore latency is the dominant cold-start bottleneck. We present Aquifer, the first system to serve MicroVM snapshots from a hierarchical CXL+RDMA memory pool. A characterization of snapshot images reveals that the vast majority of pages are either zero or cold, enabling a hotness-based snapshot format that eliminates zero pages and places only the hot working set in the CXL pool while storing cold pages in the RDMA pool. Sharing these snapshots across hosts on CXL 2.0 multi-headed devices, which lack hardware cache coherence, requires Aquifer's ownership-based coherence protocol to ensure correctness. Finally, Aquifer uses a copy-based page serving mechanism pre-installs hot pages from CXL memory before MicroVM resume and demand-pages cold pages asynchronously from RDMA. On emulated CXL+RDMA hardware, Aquifer achieves a 2.2x geometric-mean speedup in end-to-end invocation time over Firecracker and 1.1x over the next best alternative.

cs.DC↗

arXiv2026

Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation

Federated learning (FL) offers a promising distributed learning paradigm for internet of vehicles (IoV) applications. However, it faces challenges from communication overhead and dynamic environments. Model compression techniques reduce computing and communication burden yet create trade-offs between compression ratios and vehicle participation strategies. In this paper, we propose a lightweight FL algorithm named federated learning with dynamic low-rank adaptation (Fed-DLoRA), which is combined with low-rank adaptation (LoRA) to effectively reduce parameters and communication costs while enhancing training efficiency. The convergence analysis of Fed-DLoRA is conducted through stochastic gradient descent optimization coupled with singular value decomposition. This analysis establishes the theoretical relationships among LoRA rank, vehicular scheduling strategies and the model's convergence characteristics. Building on these insights, we formulate a joint optimization problem aimed at maximizing system performance. To address this problem, we propose an adaptive rank, bandwidth and vehicle selection (ARBVS) algorithm that integrates enumeration with greedy optimization strategies. The algorithm provides efficient rank selection and resource scheduling strategies for each FL communication round, thereby achieving effective performance improvements for the FL system. Experimental results demonstrate that Fed-DLoRA achieves superior performance compared to conventional federated learning approaches, exhibiting enhanced accuracy, faster convergence, and improved communication efficiency.

cs.LG↗

arXiv2025

SGDRC: Software-Defined Dynamic Resource Control for Concurrent DNN Inference on NVIDIA GPUs

Cloud service providers heavily colocate high-priority, latency-sensitive (LS), and low-priority, best-effort (BE) DNN inference services on the same GPU to improve resource utilization in data centers. Among the critical shared GPU resources, there has been very limited analysis on the dynamic allocation of compute units and VRAM bandwidth, mainly for two reasons: (1) The native GPU resource management solutions are either hardware-specific, or unable to dynamically allocate resources to different tenants, or both; (2) NVIDIA doesn't expose interfaces for VRAM bandwidth allocation, and the software stack and VRAM channel architectures are black-box, both of which limit the software-level resource management. These drive prior work to design either conservative sharing policies detrimental to throughput, or static resource partitioning only applicable to a few GPU models. To bridge this gap, this paper proposes SGDRC, a fully software-defined dynamic VRAM bandwidth and compute unit management solution for concurrent DNN inference services. SGDRC aims at guaranteeing service quality, maximizing the overall throughput, and providing general applicability to NVIDIA GPUs. SGDRC first reveals a general VRAM channel hash mapping architecture of NVIDIA GPUs through comprehensive reverse engineering and eliminates VRAM channel conflicts using software-level cache coloring. SGDRC applies bimodal tensors and tidal SM masking to dynamically allocate VRAM bandwidth and compute units, and guides the allocation of resources based on offline profiling. We evaluate 11 mainstream DNNs with real-world workloads on two NVIDIA GPUs. The results show that compared with the state-of-the-art GPU sharing solutions, SGDRC achieves the highest SLO attainment rates (99.0% on average), and improves overall throughput by up to 1.47x and BE job throughput by up to 2.36x.

cs.DC↗

arXiv2024

Tuning Fast Memory Size based on Modeling of Page Migration for Tiered Memory

Tiered memory, built upon a combination of fast memory and slow memory, provides a cost-effective solution to meet ever-increasing requirements from emerging applications for large memory capacity. Reducing the size of fast memory is valuable to improve memory utilization in production and reduce production costs because fast memory tends to be expensive. However, deciding the fast memory size is challenging because there is a complex interplay between application characterization and the overhead of page migration used to mitigate the impact of limited fast memory capacity. In this paper, we introduce a system, Tuna, to decide fast memory size based on modeling of page migration. Tuna uses micro-benchmarking to model the impact of page migration on application performance using three metrics. Tuna decides the fast memory size based on offline modeling results and limited information on workload telemetry. Evaluating with common big-memory applications and using 5% as the performance loss target, we show that Tuna in combination with a page management system (TPP) saves fast memory by 8.5% on average (up to 16%). This is in contrast to the 5% saving in fast memory reported by Microsoft Pond for the same workloads (BFS and SSSP) and the same performance loss target.

cs.PF↗

arXiv2024

Dissecting CXL Memory Performance at Scale: Analysis, Modeling, and Optimization

We present SupMario, a characterization framework designed to thoroughly analyze, model, and optimize CXL memory performance. SupMario is based on extensive evaluation of 265 workloads spanning 4 real CXL devices within 7 memory latency configurations across 4 processor platforms. SupMario uncovers many key insights, including detailed workload performance at sub-us memory latencies (140-410 ns), CXL tail latencies, CPU tolerance to CXL latencies, CXL performance root-cause analysis and precise performance prediction models. In particular, SupMario performance models rely solely on 12 CPU performance counters and accurately fit over 99% and 91%-94% workloads with a 10% misprediction target for NUMA and CXL memory, respectively. We demonstrate the practical utility of SupMario characterization findings, models, and insights by applying them to popular CXL memory management schemes, such as page interleaving and tiering policies, to identify system inefficiencies during runtime. We introduce a novel ``bestshot'' page interleaving policy and a regulated page tiering policy (Alto) tailored for memory bandwidth- and latency-sensitive workloads. In bandwidth bound scenarios, our ``best-shot'' interleaving, guided by our novel performance prediction model, achieves close-to optimal scenarios by exploiting the aggregate system and CXL/NUMA memory bandwidth. For latency sensitive workloads, Alto, driven by our key insight of utilizing ``amortized'' memory latency to regulate unnecessary page migrations, achieves up to 177% improvement over state-of-the-art memory tiering systems like TPP, as demonstrated through extensive evaluation with 8 real-world applications.

cs.OS↗

arXiv2022

Pond: CXL-Based Memory Pooling Systems for Cloud Platforms

Public cloud providers seek to meet stringent performance requirements and low hardware cost. A key driver of performance and cost is main memory. Memory pooling promises to improve DRAM utilization and thereby reduce costs. However, pooling is challenging under cloud performance requirements. This paper proposes Pond, the first memory pooling system that both meets cloud performance goals and significantly reduces DRAM cost. Pond builds on the Compute Express Link (CXL) standard for load/store access to pool memory and two key insights. First, our analysis of cloud production traces shows that pooling across 8-16 sockets is enough to achieve most of the benefits. This enables a small-pool design with low access latency. Second, it is possible to create machine learning models that can accurately predict how much local and pool memory to allocate to a virtual machine (VM) to resemble same-NUMA-node memory performance. Our evaluation with 158 workloads shows that Pond reduces DRAM costs by 7% with performance within 1-5% of same-NUMA-node VM allocations.

cs.OS↗