arXiv subjects

Dirk Kutscher

Publications and source records attributed to Dirk Kutscher.

15 recordsLinked to original sources

arXiv2026

MD2G-Cast: Relay-Coordinated Multicast for Scalable Volumetric Streaming over MoQ

Volumetric streaming remains difficult to scale because receivers with overlapping fields of view are often served independently, causing repeated transmission of shared content. We present MD2G-Cast, a relay-coordinated multicast framework over Media over QUIC with an application-aware control layer for scalable multi-user volumetric delivery. MD2G-Cast jointly uses viewing overlap, receiver capability, and bandwidth conditions to form reusable multicast groups, share common Base content, and selectively admit Enhanced delivery. We formulate grouping and Enhanced admission as a sequential control problem, realize it with Proximal Policy Optimization (PPO), and train a compact relay model with teacher guidance for Enhanced admission. We implement MD2G-Cast with real MoQ processes and evaluate it with real access and 6DoF viewing traces for up to 100 users. At 20 and 100 users, MD2G-Cast keeps the receiver-side $P_{99}$ delivery interval below 40 ms across all seven access profiles, while Rolling reaches the 500 ms reporting cap in most cases. Across the evaluated user scales, MD2G-Cast achieves the highest or tied-highest mean system utility under homogeneous access and the highest mean utility under heterogeneous access, while reducing aggregate link load by about 27% relative to Clustering at 100 users. A matched relay-control ablation separates the control structure from its optimizer, showing that random feasible actions reduce utility while deterministic control remains competitive with PPO. Together, the results support relay coordination and selective Enhanced admission, rather than a particular policy optimizer, as the central design contribution.

eess.IV↗

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↗

arXiv2026

COMETS: Coordinated Multi-Destination Video Transmission with In-Network Rate Adaptation

Large-scale video streaming events attract millions of simultaneous viewers, stressing existing delivery infrastructures. Client-driven adaptation reacts slowly to shared congestion, while server-based coordination introduces scalability bottlenecks and single points of failure. We present COMETS, a coordinated multi-destination video transmission framework that leverages information-centric networking principles such as request aggregation and in-network state awareness to enable scalable, fair, and adaptive rate control. COMETS introduces a novel range-interest protocol and distributed in-network decision process that aligns video quality across receiver groups while minimizing redundant transmissions. To achieve this, we develop a lightweight distributed optimization framework that guides per-hop quality adaptation without centralized control. Extensive emulation shows that COMETS consistently improves bandwidth utilization, fairness, and user-perceived quality of experience over DASH, MoQ, and ICN baselines, particularly under high concurrency. The results highlight COMETS as a practical, deployable approach for next-generation scalable video delivery.

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↗

arXiv2024

Affordable HPC: Leveraging Small Clusters for Big Data and Graph Computing

This study explores strategies for academic researchers to optimize computational resources within limited budgets, focusing on building small, efficient computing clusters. It delves into the comparative costs of purchasing versus renting servers, guided by market research and economic theories on tiered pricing. The paper offers detailed insights into the selection and assembly of hardware components such as CPUs, GPUs, and motherboards tailored to specific research needs. It introduces innovative methods to mitigate the performance issues caused by PCIe switch bandwidth limitations in order to enhance GPU task scheduling. Furthermore, a Graph Neural Network (GNN) framework is proposed to analyze and optimize parallelism in computing networks.

cs.AR↗

arXiv2024

Secure Web Objects: Building Blocks for Metaverse Interoperability and Decentralization

This position paper explores how to support the Web's evolution through an underlying data-centric approach that better matches the data-orientedness of modern and emerging applications. We revisit the original vision of the Web as a hypermedia system that supports document composability and application interoperability via name-based data access. We propose the use of secure web objects (SWO), a data-oriented communication approach that can reduce complexity, centrality, and inefficiency, particularly for collaborative and local-first applications, such as the Metaverse and other collaborative applications. SWO are named, signed, application-defined objects that are secured independently of their containers or communications channels, an approach that leverages the results from over a decade-long data-centric networking research. This approach does not require intermediation by aggregators of identity, storage, and other services that are common today. We present a brief design overview, illustrated through prototypes for two editors of shared hypermedia documents: one for 3D and one for LaTeX. We also discuss our findings and suggest a roadmap for future research.

cs.NI↗

arXiv2023

Statement: The Metaverse as an Information-Centric Network

This paper discusses challenges and opportunities of considering the Metaverse as an Information-Centric Network (ICN). The Web today essentially represents a data-centric application layer: data named by URLs is manipulated with REST primitives. However, the semantic gap with the underlying host-oriented transport is significant, typically leading to complexity, centralization, and brittleness. Popular interest in "the Metaverse" suggests that the end-user experience of the Web will evolve towards always-on eXtended Reality (XR). With the benefit of a historical perspective, computing advances, and decades of experience with a global network, there is an opportunity to holistically consider the Metaverse not as an application of the current network, but an evolution of the network itself, reducing rather than widening the gap between network architecture and application semantics. An ICN architecture offers the possibility to achieve this with less overhead, low latency, better security, and more disruption tolerance suitable to diverse uses cases, even those facing intermittent connectivity.

cs.NI↗

arXiv2023

SoK: Distributed Computing in ICN

Information-Centric Networking (ICN), with its data-oriented operation and generally more powerful forwarding layer, provides an attractive platform for distributed computing. This paper provides a systematic overview and categorization of different distributed computing approaches in ICN encompassing fundamental design principles, frameworks and orchestration, protocols, enablers, and applications. We discuss current pain points in legacy distributed computing, attractive ICN features, and how different systems use them. This paper also provides a discussion of potential future work for distributed computing in ICN.

cs.NI↗

arXiv2022

Delay-Tolerant ICN and Its Application to LoRa

Connecting long-range wireless networks to the Internet imposes challenges due to vastly longer round-trip-times (RTTs). In this paper, we present an ICN protocol framework that enables robust and efficient delay-tolerant communication to edge networks. Our approach provides ICN-idiomatic communication between networks with vastly different RTTs. We applied this framework to LoRa, enabling end-to-end consumer-to-LoRa-producer interaction over an ICN-Internet and asynchronous data production in the LoRa edge. Instead of using LoRaWAN, we implemented an IEEE 802.15.4e DSME MAC layer on top of the LoRa PHY and ICN protocol mechanisms in RIOT OS. Executed on off-the-shelf IoT hardware, we provide a comparative evaluation for basic NDN-style ICN [60], RICE [31]-like pulling, and reflexive forwarding [46]. This is the first practical evaluation of ICN over LoRa using a reliable MAC. Our results show that periodic polling in NDN works inefficiently when facing long and differing RTTs. RICE reduces polling overhead and exploits gateway knowledge, without violating ICN principles. Reflexive forwarding reflects sporadic data generation naturally. Combined with a local data push, it operates efficiently and enables lifetimes of >1 year for battery powered LoRa-ICN nodes.

cs.NI↗

arXiv2022

Long-Range ICN for the IoT: Exploring a LoRa System Design

This paper presents LoRa-ICN, a comprehensive IoT networking system based on a common long-range communication layer (LoRa) combined with Information-Centric Networking (ICN) principles. We have replaced the LoRaWAN MAC layer with an IEEE 802.15.4 Deterministic and Synchronous Multi-Channel Extension (DSME). This multifaceted MAC layer allows for different mappings of ICN message semantics, which we explore to enable new LoRa cenarios. We designed LoRa-ICN from the ground-up to improve reliability and to reduce dependency on centralized components in LoRa IoT scenarios. We have implemented a feature-complete prototype in a common network simulator to validate our approach. Our results show design trade-offs of different mapping alternatives in terms of robustness and efficiency.

cs.NI↗