arXiv subjects

Xinjiao Li

Publications and source records attributed to Xinjiao Li.

7 recordsLinked to original sources

arXiv2026

MARS: Multipath Adaptive Reliable Service

Multipath transport is important for Internet/WAN services that move data volumes across heterogeneous paths, including geo-distributed analytics, content distribution, and cloud-service pipelines. Existing solutions face a trade-off: end-to-end transports such as MPTCP and MPQUIC are deployable but limited by endpoint-visible paths and delayed congestion feedback, while routing- or forwarder-assisted approaches often require infrastructure support or lack safe coordination across forwarding choices. This paper presents MARS, a receiver-driven, forwarder-assisted multipath transport. MARS combines tier-synchronized overlay path discovery with coupled consumer/forwarder congestion control, enabling it to expand usable forwarding opportunities and react near bottlenecks. It runs as an incrementally deployable UDP overlay at clients, servers, relays, or CDN-like nodes. We implement MARS in simulation and as a prototype, and evaluate it through simulation and Mininet emulation across deployment scopes, loss rates, and a forwarding-face outage scenario. Results show MARS provides deployment-dependent benefits: with endpoint-only deployment, it performs comparably to the evaluated ECMP-limited configurations of MPTCP and MPQUIC. With cooperating overlay forwarders, it expands the usable path set from routing-exposed forwarding candidates. Across the tested loss conditions, it reduces maximum T95 by up to 66.7% and 63.9% relative to the evaluated path-expanded MPTCP and MPQUIC configurations, respectively, given the same path set. Path discovery remains lightweight, flow fairness remains high, and MARS degrades gracefully during an emulated forwarding-face outage and recovers quickly after face restoration. Overall, ICN-style receiver-driven forwarding can serve as a deployable overlay transport substrate for coordinated WAN multipath without requiring changes to IP routing.

cs.NI↗

arXiv2026

MultiMoQ: Multi-Access Media-Over-QUIC for Robust Immersive Video Streaming

Live immersive video streaming, particularly 360-degree video, is increasingly adopted in applications such as virtual events, sports broadcasting, and remote education. Existing approaches struggle to support high-bitrate immersive streaming for large numbers of concurrent users, with coarse-grained delivery limiting responsiveness and insufficient support for coordinating concurrent tile streams. Media over QUIC (MoQ) has recently emerged as a promising solution for large-scale media delivery, yet it lacks robustness under bandwidth-constrained conditions, often resulting in playback stalls. To address these challenges, we present MultiMoQ, a multi-access tile streaming framework built on MoQ that redesigns its delivery mechanism for robust high-bitrate streaming across multiple access paths while supporting flexible tile scheduling and seamless access switching. We implement a fully functional prototype of MultiMoQ and evaluate it in network emulation under heterogeneous real-world network conditions, comparing against Dynamic Adaptive Streaming over HTTP (DASH) and standard MoQ. Results show that MultiMoQ increases goodput for enhancement tiles and base video and reduces enhancement-tile tail end-to-end latency relative to DASH, while preserving audio continuity and avoiding the persistent stalls of standard MoQ. These transport gains translate into smoother viewport playback, and the ablation results further confirm the contribution of multi-access control to playback continuity.

cs.NI↗

arXiv2025

INDS: Incremental Named Data Streaming for Real-Time Point Cloud Video

Real-time streaming of point cloud video, characterized by massive data volumes and high sensitivity to packet loss, remains a key challenge for immersive applications under dynamic network conditions. While connection-oriented protocols such as TCP and more modern alternatives like QUIC alleviate some transport-layer inefficiencies, including head-of-line blocking, they still retain a coarse-grained, segment-based delivery model and a centralized control loop that limit fine-grained adaptation and effective caching. We introduce INDS (Incremental Named Data Streaming), an adaptive streaming framework based on Information-Centric Networking (ICN) that rethinks delivery for hierarchical, layered media. INDS leverages the Octree structure of point cloud video and expressive content naming to support progressive, partial retrieval of enhancement layers based on consumer bandwidth and decoding capability. By combining time-windows with Group-of-Frames (GoF), INDS's naming scheme supports fine-grained in-network caching and facilitates efficient multi-user data reuse. INDS can be deployed as an overlay, remaining compatible with QUIC-based transport infrastructure as well as future Media-over-QUIC (MoQ) architectures, without requiring changes to underlying IP networks. Our prototype implementation shows up to 80% lower delay, 15-50% higher throughput, and 20-30% increased cache hit rates compared to state-of-the-art DASH-style systems. Together, these results establish INDS as a scalable, cache-friendly solution for real-time point cloud streaming under variable and lossy conditions, while its compatibility with MoQ overlays further positions it as a practical, forward-compatible architecture for emerging immersive media systems.

cs.MM↗

arXiv2025

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning

Training large-scale distributed machine learning models imposes considerable demands on network infrastructure, often resulting in sudden traffic spikes that lead to congestion, increased latency, and reduced throughput, which would ultimately affect convergence times and overall training performance. While gradient compression techniques are commonly employed to alleviate network load, they frequently compromise model accuracy due to the loss of gradient information. This paper introduces NetSenseML, a novel network adaptive distributed deep learning framework that dynamically adjusts quantization, pruning, and compression strategies in response to real-time network conditions. By actively monitoring network conditions, NetSenseML applies gradient compression only when network congestion negatively impacts convergence speed, thus effectively balancing data payload reduction and model accuracy preservation. Our approach ensures efficient resource usage by adapting reduction techniques based on current network conditions, leading to shorter convergence times and improved training efficiency. We present the design of the NetSenseML adaptive data reduction function and experimental evaluations show that NetSenseML can improve training throughput by a factor of 1.55 to 9.84 times compared to state-of-the-art compression-enabled systems for representative DDL training jobs in bandwidth-constrained conditions.

cs.DC↗

arXiv2025

ViFusion: In-Network Tensor Fusion for Scalable Video Feature Indexing

Large-scale video feature indexing in datacenters is critically dependent on efficient data transfer. Although in-network computation has emerged as a compelling strategy for accelerating feature extraction and reducing overhead in distributed multimedia systems, harnessing advanced networking resources at both the switch and host levels remains a formidable challenge. These difficulties are compounded by heterogeneous hardware, diverse application requirements, and complex multipath topologies. Existing methods focus primarily on optimizing inference for large neural network models using specialized collective communication libraries, which often face performance degradation in network congestion scenarios. To overcome these limitations, we present ViFusion, a communication aware tensor fusion framework that streamlines distributed video indexing by merging numerous small feature tensors into consolidated and more manageable units. By integrating an in-network computation module and a dedicated tensor fusion mechanism within datacenter environments, ViFusion substantially improves the efficiency of video feature indexing workflows. The deployment results show that ViFusion improves the throughput of the video retrieval system by 8--22 times with the same level of latency as state-of-the-art systems.

cs.MM↗

arXiv2025

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization

Hybrid parallelism techniques are essential for efficiently training large language models (LLMs). Nevertheless, current automatic parallel planning frameworks often overlook the simultaneous consideration of node heterogeneity and dynamic network topology changes, limiting their effectiveness in practical applications. In this paper, we address these limitations by modeling heterogeneous nodes within dynamically changing network environments and leveraging simulation-based strategies to determine optimal parallel configurations. Our approach enables fine-grained workload allocation tailored for heterogeneous nodes and complex network scenarios, achieving performance competitive with state-of-the-art methods under regular and stable network conditions. Additionally, we introduce a strategy pruning technique to rapidly discard infeasible parallel configurations, substantially reducing the search space and accelerating the search process through parallel execution within the simulator. Preliminary evaluations confirm that our method notably enhances training performance on heterogeneous nodes and demonstrates improved adaptability in complex, dynamic scenarios such as cloud computing environments.

cs.DC↗

arXiv2025

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning

Large-scale deep neural networks (DNN) exhibit excellent performance for various tasks. As DNNs and datasets grow, distributed training becomes extremely time-consuming and demands larger clusters. A main bottleneck is the resulting gradient aggregation overhead. While gradient compression and sparse collective communication techniques are commonly employed to alleviate network load, many gradient compression schemes do not achieve acceleration of the training process while also preserving accuracy. This paper introduces PacTrain, a novel framework that accelerates distributed training by combining pruning with sparse gradient compression. Active pruning of the neural network makes the model weights and gradients sparse. By ensuring the global knowledge of the gradient sparsity among all distributed training workers, we can perform lightweight compression communication without harming accuracy. We show that the PacTrain compression scheme achieves a near-optimal compression strategy while remaining compatible with the all-reduce primitive. Experimental evaluations show that PacTrain improves training throughput by 1.25 to 8.72 times compared to state-of-the-art compression-enabled systems for representative vision and language models training tasks under bandwidth-constrained conditions.

cs.DC↗