arXiv subjects

Wenhai Lin

Publications and source records attributed to Wenhai Lin.

5 recordsLinked to original sources

arXiv2026

Throughput Characterization of Wireless CSMA Networks With Arbitrary Sensing and Interference Topologies

The performance analysis of wireless CSMA networks is notoriously difficult due to the intricate sensing and interference relationships among links. Even the fundamental problem of throughput characterization remains open when sensing and interference topologies are both arbitrary. In this paper, we develop a new analytical framework for throughput characterization in wireless CSMA networks with arbitrary sensing and interference topologies. The proposed framework yields explicit throughput expressions without relying on the commonly adopted zero-propagation-delay assumption. The key idea is to exploit the clique structure of the sensing graph to transform the original CSMA network into an equivalent multi-channel network, and then model its dynamics through a discrete-time Markov renewal process. In this way, the framework explicitly captures global coupling among links and enables analytical evaluation of how access parameters affect network performance. The proposed analysis is applied to several representative CSMA scenarios, including networks with multi-BSS IEEE 802.11 networks with universal frequency reuse, and ad-hoc topologies exhibiting hidden-terminal, exposed-terminal, and flow-in-the-middle effects. Simulation results show that, in dense deployments and in scenarios with strong coupling among link behaviors, the proposed model significantly outperforms existing analytical approaches in throughput estimation and enables more accurate determination of access parameters.

cs.NI↗

arXiv2025

AugServe: Adaptive Request Scheduling for Augmented Large Language Model Inference Serving

As augmented large language models (LLMs) with external tools become increasingly popular in web applications, improving augmented LLM inference serving efficiency and optimizing service-level objectives (SLOs) are critical for enhancing user experience. To achieve this, inference systems must maximize request handling within latency constraints, referred to as increasing effective throughput. However, existing systems face two major challenges: (i) reliance on first-come-first-served (FCFS) scheduling causes severe head-of-line blocking, leading to queuing delays exceeding the SLOs for many requests; and (ii) static batch token limit, which fails to adapt to fluctuating loads and hardware conditions. Both of these factors degrade effective throughput and service quality. This paper presents AugServe, an efficient inference framework designed to reduce queueing latency and enhance effective throughput for augmented LLM inference services. The core idea of AugServe is a two-stage adaptive request scheduling strategy. Specifically, AugServe combines the inference features of augmented LLM requests to optimize the order of scheduling decisions (stage I). These decisions are continuously refined with runtime information (stage II), adapting to both request characteristics and system capabilities. In addition, AugServe dynamically adjusts the token batching mechanism based on hardware status and real-time load, further enhancing throughput performance. Experimental results show that AugServe achieves 4.7x and 3.3x higher effective throughput than vLLM and InferCept, while reducing time-to-first-token (TTFT) by up to 96.3% and 95.0%, respectively.

cs.CL↗

arXiv2025

GoCkpt: Gradient-Assisted Multi-Step overlapped Checkpointing for Efficient LLM Training

The accuracy of large language models (LLMs) improves with increasing model size, but increasing model complexity also poses significant challenges to training stability. Periodic checkpointing is a key mechanism for fault recovery and is widely used in LLM training. However, traditional checkpointing strategies often pause or delay GPU computation during checkpoint saving for checkpoint GPU-CPU transfer, resulting in significant training interruptions and reduced training throughput. To address this issue, we propose GoCkpt, a method to overlap checkpoint saving with multiple training steps and restore the final checkpoint on the CPU. We transfer the checkpoint across multiple steps, each step transfers part of the checkpoint state, and we transfer some of the gradient data used for parameter updates. After the transfer is complete, each partial checkpoint state is updated to a consistent version on the CPU, thus avoiding the checkpoint state inconsistency problem caused by transferring checkpoints across multiple steps. Furthermore, we introduce a transfer optimization strategy to maximize GPU-CPU bandwidth utilization and SSD persistence throughput. This dual optimization overlapping saves across steps and maximizing I/O efficiency significantly reduces invalid training time. Experimental results show that GoCkpt can increase training throughput by up to 38.4% compared to traditional asynchronous checkpoint solutions in the industry. We also find that GoCkpt can reduce training interruption time by 86.7% compared to the state-of-the-art checkpoint transfer methods, which results in a 4.8% throughput improvement.

cs.OS↗

arXiv2025

Harmonious Coexistence between Aloha and CSMA: Novel Dual-channel Modeling and Throughput Optimization

The scarcity of the licensed spectrum is forcing emerging Internet of Things (IoT) networks to operate within the unlicensed spectrum. Yet there has been extensive observation indicating that performance deterioration and significant unfairness would arise, when newly deployed Aloha-based networks coexist with incumbent Carrier Sense Multiple Access (CSMA)-based WiFi networks, especially without proper adjustment of packet transmission times. Therefore, ensuring harmonious cohabitation between Aloha and CSMA networks is of paramount importance. How to properly tune system parameters to guarantee harmonious coexistence between these two networks, nevertheless, remains largely unexplored. To address the above open issue, this paper proposed a novel dual-channel analytical framework to characterize the throughput performance of the cohabitation between slotted Aloha and CSMA networks. To achieve harmonious coexistence, the total throughput of the coexisting network under a given desired throughput proportion is optimized by tuning the packet transmission time of CSMA nodes and transmission probabilities. The optimization results indicate that the packet transmission time of CSMA nodes should be set slightly less than that of Aloha nodes. The proposed framework is further applied to enhance the network throughput and fairness of the cohabitation of LTE Unlicensed and WiFi networks.

cs.NI↗

arXiv2023

High-performance and Scalable Software-based NVMe Virtualization Mechanism with I/O Queues Passthrough

NVMe(Non-Volatile Memory Express) is an industry standard for solid-state drives (SSDs) that has been widely adopted in data centers. NVMe virtualization is crucial in cloud computing as it allows for virtualized NVMe devices to be used by virtual machines (VMs), thereby improving the utilization of storage resources. However, traditional software-based solutions have flexibility benefits but often come at the cost of performance degradation or high CPU overhead. On the other hand, hardware-assisted solutions offer high performance and low CPU usage, but their adoption is often limited by the need for special hardware support or the requirement for new hardware development. In this paper, we propose LightIOV, a novel software-based NVMe virtualization mechanism that achieves high performance and scalability without consuming valuable CPU resources and without requiring special hardware support. LightIOV can support thousands of VMs on each server. The key idea behind LightIOV is NVMe hardware I/O queues passthrough, which enables VMs to directly access I/O queues of NVMe devices, thus eliminating virtualization overhead and providing near-native performance. Results from our experiments show that LightIOV can provide comparable performance to VFIO, with an IOPS of 97.6%-100.2% of VFIO. Furthermore, in high-density VMs environments, LightIOV achieves 31.4% lower latency than SPDK-Vhost when running 200 VMs, and an improvement of 27.1% in OPS performance in real-world applications.

cs.AR↗