74 Search Results for "Sun, Kevin"

Document
DOI: 10.4230/LIPIcs.SoCG.2026.90
Better Sampling Bounds for Restricted Delaunay Triangulations and a Star-Shaped Property for Restricted Voronoi Cells

Authors: Jonathan Richard Shewchuk

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
The restricted Delaunay triangulation of a closed surface Σ and a finite point set V ⊂ Σ is a subcomplex of the Delaunay tetrahedralization of V whose triangles approximate Σ. It is well known that if V is a sufficiently dense sample of a smooth Σ, then the union of the restricted Delaunay triangles is homeomorphic to Σ. We show that an ε-sample with ε ≤ 0.3245 suffices. By comparison, Dey proves it for a 0.18-sample; our improved sampling bound reduces the number of sample points required by a factor of 3.25. More importantly, we improve a related sampling bound of Cheng et al. for Delaunay surface meshing, reducing the number of sample points required by a factor of 21. The first step of our homeomorphism proof is particularly interesting: we show that for a 0.44-sample, the restricted Voronoi cell of each site v ∈ V is homeomorphic to a disk, and the orthogonal projection of the cell onto T_vΣ (the plane tangent to Σ at v) is star-shaped.
Cite as

Jonathan Richard Shewchuk. Better Sampling Bounds for Restricted Delaunay Triangulations and a Star-Shaped Property for Restricted Voronoi Cells. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 90:1-90:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{shewchuk:LIPIcs.SoCG.2026.90,
  author =	{Shewchuk, Jonathan Richard},
  title =	{{Better Sampling Bounds for Restricted Delaunay Triangulations and a Star-Shaped Property for Restricted Voronoi Cells}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{90:1--90:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.90},
  URN =		{urn:nbn:de:0030-drops-258961},
  doi =		{10.4230/LIPIcs.SoCG.2026.90},
  annote =	{Keywords: Restricted Delaunay triangulation, restricted Voronoi diagram, surface sampling, surface mesh generation, surface reconstruction, \epsilon-sample, homeomorphism}
}
Document
DOI: 10.4230/OASIcs.FMBC.2026.5
Smart Tests for a Smart Contract Language

Authors: Miguel Valido and António Ravara

Published in: OASIcs, Volume 142, 7th International Workshop on Formal Methods for Blockchains (FMBC 2026)

Abstract
Smart contracts are high-stakes software: their immutable, publicly accessible, code may govern assets worth millions, meaning that even minor defects can have severe consequences. The most used techniques to ensure smart contract correctness are testing and formal verification. Testing is almost always employed but is often restricted to unit tests (which often miss edge cases) and has limited coverage, while formal verification can provide strong guarantees but is often costly and complex to apply, demanding substantial time and expertise. Property-based testing bridges this gap by exploring large input spaces and shrinking failures to minimal counterexamples, helping uncover defects early in development. Formal verification can be left to critical features once testing has filtered out common issues. To add to the challenges smart contract developers face, most languages used were not designed with safety and security guarantees built-in. Daml is a smart contract language designed with correctness in mind, featuring a strong static type system, functional programming paradigms, and built-in abstractions for common smart contract patterns. However, Daml currently lacks support for property-based testing, limiting developers' ability to systematically explore input spaces and verify contract properties. This paper introduces Hypothesis2Daml, an open-source library that brings property-based testing to the Daml ecosystem by connecting the Hypothesis testing framework with the Daml JSON API. Hypothesis2Daml enables developers to specify invariants, preconditions, and stateful workflows over realistic ledger interactions, while providing automatic input generation, shrinking, and isolation of ledger state between test cases. The approach is evaluated using a benchmark consisting of eight contracts, three Daml templates, and twenty-eight property-based tests covering happy paths, negative cases, and alternative interaction orders. The results show that property-based testing is feasible for Daml smart contracts, can systematically expose violated properties with minimal counterexamples, and supports effective debugging of realistic, stateful workflows.
Cite as

Miguel Valido and António Ravara. Smart Tests for a Smart Contract Language. In 7th International Workshop on Formal Methods for Blockchains (FMBC 2026). Open Access Series in Informatics (OASIcs), Volume 142, pp. 5:1-5:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{valido_et_al:OASIcs.FMBC.2026.5,
  author =	{Valido, Miguel and Ravara, Ant\'{o}nio},
  title =	{{Smart Tests for a Smart Contract Language}},
  booktitle =	{7th International Workshop on Formal Methods for Blockchains (FMBC 2026)},
  pages =	{5:1--5:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-424-6},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{142},
  editor =	{Bartoletti, Massimo and Marmsoler, Diego},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.FMBC.2026.5},
  URN =		{urn:nbn:de:0030-drops-257021},
  doi =		{10.4230/OASIcs.FMBC.2026.5},
  annote =	{Keywords: Smart contracts, Blockchain, Daml, Property-based testing, Hypothesis}
}
Document
DOI: 10.4230/LIPIcs.FUN.2026.1
Finding Maximum and Minimum Size Matrices: The Algorithmic Complexity of Coding Challenges

Authors: Abdelrahman Abdelmonsef, Xingyu Dong, Daniel Průša, Michael Wehar, and Chen Xu

Published in: LIPIcs, Volume 366, 13th International Conference on Fun with Algorithms (FUN 2026)

Abstract
We investigate coding challenge problems related to finding a maximum (or minimum) size match for a predetermined two-dimensional pattern. We improve upon known runtime results by introducing new algorithms and refining existing algorithmic techniques. First, we present our main result which introduces a nearly quadratic time algorithm for the problem of finding a rectangular block within a matrix of maximum (or minimum) size containing only positive border entries which improves the prior cubic time solution. Then, we introduce a quadratic time and linear space algorithm for the problem of finding all rectangular blocks containing only positive border and empty interior entries. Finally, we present a log(n) factor improvement for detecting the largest length such that all square blocks in a matrix have their sums bounded by a given number.
Cite as

Abdelrahman Abdelmonsef, Xingyu Dong, Daniel Průša, Michael Wehar, and Chen Xu. Finding Maximum and Minimum Size Matrices: The Algorithmic Complexity of Coding Challenges. In 13th International Conference on Fun with Algorithms (FUN 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 366, pp. 1:1-1:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{abdelmonsef_et_al:LIPIcs.FUN.2026.1,
  author =	{Abdelmonsef, Abdelrahman and Dong, Xingyu and Pr\r{u}\v{s}a, Daniel and Wehar, Michael and Xu, Chen},
  title =	{{Finding Maximum and Minimum Size Matrices: The Algorithmic Complexity of Coding Challenges}},
  booktitle =	{13th International Conference on Fun with Algorithms (FUN 2026)},
  pages =	{1:1--1:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-417-8},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{366},
  editor =	{Iacono, John},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FUN.2026.1},
  URN =		{urn:nbn:de:0030-drops-257203},
  doi =		{10.4230/LIPIcs.FUN.2026.1},
  annote =	{Keywords: Pattern Matching, Matrices, Discrete Algorithms}
}
Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2026.2
SLURM-Managed HyperParameter Optimization

Authors: Anusha Chattopadhyay, Hendrik Borras, Bernhard Klein, and Holger Fröning

Published in: OASIcs, Volume 141, 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026)

Abstract
Hyperparameter optimization (HPO) is essential for achieving state-of-the-art performance in machine learning, yet it is computationally demanding, particularly on shared or resource-constrained clusters. We present a system that integrates the Asynchronous Successive Halving Algorithm (ASHA) with SEML, the SLURM Experiment Management Library - an experiment orchestration layer that provides declarative configuration, provenance, metric tracking, and robust SLURM job management. The resulting open-source tool enables scalable, fault-tolerant HPO on SLURM-managed infrastructure: SEML handles experiment specification, versioning, and scheduling, while ASHA performs asynchronous early stopping and resource reallocation to concentrate computation on promising configurations. Overall, the system streamlines experiment lifecycle management, enables distributed evaluations with minimal manual effort, and reduces the time required to reach high-quality configurations compared to conventional Grid and Random Search methods under similar compute budgets.
Cite as

Anusha Chattopadhyay, Hendrik Borras, Bernhard Klein, and Holger Fröning. SLURM-Managed HyperParameter Optimization. In 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026). Open Access Series in Informatics (OASIcs), Volume 141, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chattopadhyay_et_al:OASIcs.PARMA-DITAM.2026.2,
  author =	{Chattopadhyay, Anusha and Borras, Hendrik and Klein, Bernhard and Fr\"{o}ning, Holger},
  title =	{{SLURM-Managed HyperParameter Optimization}},
  booktitle =	{17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026)},
  pages =	{2:1--2:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-416-1},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{141},
  editor =	{Baroffio, Davide and Busia, Paola and Denisov, Lev and Shukla, Nitin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2026.2},
  URN =		{urn:nbn:de:0030-drops-256697},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2026.2},
  annote =	{Keywords: Hyperparameter optimization, Asynchronous Successive Halving Algorithm (ASHA), Experiment management, SLURM, SEML, Open Source}
}
Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2026.8
Performance Modeling & Mapping of LLM Inference on Heterogeneous Vectorized CGRAs

Authors: Dionysios Kefallinos, Georgios Alexandris, Alexis Maras, Panagiotis Chaidos, Manil Dev Gomony, Henk Corporaal, Dimitrios Soudris, and Sotirios Xydis

Published in: OASIcs, Volume 141, 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026)

Abstract
Since the emergence of transformer-based models, the computational demands for Large Language Model (LLM) inference have been increasing exponentially, primarily due to their compounding parameter sizes, their structural complexity, and the use of non-linear functions. This tendency leads to the necessity of deploying them on low-power edge devices and DNN accelerators, to fuel next-generation agentic AI systems. Coarse-Grained Reconfigurable Architectures (CGRAs) have proven to be a compelling paradigm for edge acceleration, combining the programmability of general-purpose platforms with the high performance and energy efficiency associated with ASICs. In this work, we introduce an end-to-end performance modeling and mapping framework for LLM inference on heterogeneous CGRAs. Our methodology enables rapid exploration of the micro-architectural design space parameters, i.e., the number of processing elements, vector sizes, and memory configurations, by providing an accurate, explainable, and analytical CGRA performance modeling methodology, with an average cycle error of 0.9%. Architecturally, we build upon R-Blocks, a heterogeneous CGRA platform, and extend it to support floating-point arithmetic operations as well as a full-stack compilation and mapping flow for both full (FP32) and quantized (INT8) Llama2 models. The proposed methodology, evaluated on a 22nm technology node, achieves superior peak performance per Watt compared to related works such as REVAMP and CFEACT (1.8× and 2.8× respectively).
Cite as

Dionysios Kefallinos, Georgios Alexandris, Alexis Maras, Panagiotis Chaidos, Manil Dev Gomony, Henk Corporaal, Dimitrios Soudris, and Sotirios Xydis. Performance Modeling & Mapping of LLM Inference on Heterogeneous Vectorized CGRAs. In 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026). Open Access Series in Informatics (OASIcs), Volume 141, pp. 8:1-8:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kefallinos_et_al:OASIcs.PARMA-DITAM.2026.8,
  author =	{Kefallinos, Dionysios and Alexandris, Georgios and Maras, Alexis and Chaidos, Panagiotis and Gomony, Manil Dev and Corporaal, Henk and Soudris, Dimitrios and Xydis, Sotirios},
  title =	{{Performance Modeling \& Mapping of LLM Inference on Heterogeneous Vectorized CGRAs}},
  booktitle =	{17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026)},
  pages =	{8:1--8:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-416-1},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{141},
  editor =	{Baroffio, Davide and Busia, Paola and Denisov, Lev and Shukla, Nitin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2026.8},
  URN =		{urn:nbn:de:0030-drops-256752},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2026.8},
  annote =	{Keywords: Edge AI, LLM, CGRA, Heterogeneous Architectures, Performance Modeling, Hardware Acceleration, Low Power Computing}
}
Document
Research
DOI: 10.4230/TGDK.4.1.1
On the Computational Cost of Knowledge Graph Embeddings

Authors: Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann

Published in: TGDK, Volume 4, Issue 1 (2026). Transactions on Graph Data and Knowledge, Volume 4, Issue 1

Abstract
Over a decade, numerous Knowledge Graph Embedding (KGE) models have been designed and evaluated on reference datasets, always with increasing performance. In this paper, we re-evaluate these models with respect to their computational efficiency during training, by estimating the computational cost of the procedure expressed in floating-point operations. We design a cost model based on analytical expressions and apply it on a collection of 20 KGE models, representative of the state-of-the-art. We show that dimensionality or parameter efficiency, used in the literature to compare models with each other, are not suitable to evaluate the true cost of models. Through fixed-budget experiments, a novel approach to evaluate KGE models based on cost estimates, we re-assess the relative performance of model families compared to the state-of-the-art. Bilinear models such as ComplEx underperform with a low computational budget while hyperbolic linear models appear to offer no particular benefit compared to simpler Euclidian models, especially the MuRE model. Neural models, such as ConvE or CompGCN, achieve reasonable performance in the literature but their high computational cost appears unnecessary when compared with other models. The trade-off between efficiency and expressivity of both linear and neural models is to be further explored.
Cite as

Victor Charpenay, Mansour Zoubeirou A Mayaki, and Antoine Zimmermann. On the Computational Cost of Knowledge Graph Embeddings. In Transactions on Graph Data and Knowledge (TGDK), Volume 4, Issue 1, pp. 1:1-1:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@Article{charpenay_et_al:TGDK.4.1.1,
  author =	{Charpenay, Victor and Zoubeirou A Mayaki, Mansour and Zimmermann, Antoine},
  title =	{{On the Computational Cost of Knowledge Graph Embeddings}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{1:1--1:30},
  ISSN =	{2942-7517},
  year =	{2026},
  volume =	{4},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.4.1.1},
  URN =		{urn:nbn:de:0030-drops-256863},
  doi =		{10.4230/TGDK.4.1.1},
  annote =	{Keywords: Knowledge Graph Embedding, Parameter Efficiency, Computational Budget, Green AI}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.9
Tight Loops, Smooth Streams: Responsive Congestion Control for Real-Time Video

Authors: Pantea Karimi, Sadjad Fouladi, Vibhaalakshmi Sivaraman, and Mohammad Alizadeh

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Real-time video streaming relies on rate control to match video bitrate to network capacity while keeping latency low. Existing deployed video rate controllers react slowly to network changes, causing under-utilization and latency spikes. In contrast, modern delay-sensitive congestion control algorithms (CCAs) adapt on round-trip-time timescales, maintaining a tight feedback loop that achieves both high utilization and low latency. We introduce Vidaptive, a lightweight framework that enables real-time video to leverage responsive CCAs without codec changes. Vidaptive decouples encoding from transmission: it paces video frames at the CCA’s rate and injects dummy packets when the encoder output is insufficient, preserving a continuous feedback loop. An online algorithm dynamically adjusts the encoder’s target bitrate to align with CCA capacity while bounding frame latency. Implemented in Google WebRTC, Vidaptive improves both video quality and tail latency on diverse cellular traces. Compared to GCC, it delivers 1.5× higher bitrate, +40% VMAF, +1.4 dB SSIM, +1.3 dB PSNR, and reduces 95th-percentile frame latency by 57% (2.2 seconds). Against Salsify, it achieves lower tail latency without invasive codec modifications. These results show that coupling existing CCAs with a thin adaptation layer can outperform specialized video rate controllers while remaining deployable in practice.
Cite as

Pantea Karimi, Sadjad Fouladi, Vibhaalakshmi Sivaraman, and Mohammad Alizadeh. Tight Loops, Smooth Streams: Responsive Congestion Control for Real-Time Video. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 9:1-9:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{karimi_et_al:OASIcs.NINeS.2026.9,
  author =	{Karimi, Pantea and Fouladi, Sadjad and Sivaraman, Vibhaalakshmi and Alizadeh, Mohammad},
  title =	{{Tight Loops, Smooth Streams: Responsive Congestion Control for Real-Time Video}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{9:1--9:29},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.9},
  URN =		{urn:nbn:de:0030-drops-255942},
  doi =		{10.4230/OASIcs.NINeS.2026.9},
  annote =	{Keywords: real-time video, congestion control, transport protocols, video rate control, low-latency video communication, tight feedback loop}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.12
No Signal to Rule Them All: A Systematic Analysis of In-Network Congestion Signals

Authors: Sarah McClure, Nandita Dukkipati, Sylvia Ratnasamy, and Scott Shenker

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
In this paper, we address the following question: what in-network signals should a network provide to congestion control algorithms? To answer this guiding question, we use prior work to automatically generate congestion control algorithms optimized for a given performance objective and set of in-network congestion signals. We then make observations about the relative value of these congestion signals across a range of performance objectives. Our analysis yields a surprising central finding: for the average case, sophisticated In-Network Telemetry (INT) offers minimal performance benefits over traditional end-to-end (E2E) signals, with performance typically within 3%. We also find no single "best" INT signal, but rather a clear trade-off that manifests in many scenarios: link-based signals often excel at controlling delay, while queue-based signals are better for maximizing throughput. To make these findings concrete, we validate them by examining the extent to which in-network signals improve the performance of the BBR congestion control algorithm.
Cite as

Sarah McClure, Nandita Dukkipati, Sylvia Ratnasamy, and Scott Shenker. No Signal to Rule Them All: A Systematic Analysis of In-Network Congestion Signals. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 12:1-12:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{mcclure_et_al:OASIcs.NINeS.2026.12,
  author =	{McClure, Sarah and Dukkipati, Nandita and Ratnasamy, Sylvia and Shenker, Scott},
  title =	{{No Signal to Rule Them All: A Systematic Analysis of In-Network Congestion Signals}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{12:1--12:30},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.12},
  URN =		{urn:nbn:de:0030-drops-255974},
  doi =		{10.4230/OASIcs.NINeS.2026.12},
  annote =	{Keywords: Congestion control, in-network telemetry}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.13
In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication

Authors: Jianchang Su, Yifan Zhang, and Wei Zhang

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Broadcasting and aggregation dominate the communication overhead in distributed systems, from machine learning training to data analytics. Current acceleration approaches require specialized hardware (RDMA) or dedicated resources (DPDK), limiting their deployment in commodity clouds. However, we present a counter-intuitive alternative: rather than bypassing the kernel, we move operations into it using eBPF. While this imposes severe constraints including no floating-point, limited memory, and stateless execution, we show these restrictions paradoxically drive innovative protocol designs that yield unexpected benefits. We introduce AggBox, which implements broadcast and aggregation operations entirely within eBPF’s constrained environment. Our key innovations include stateless group acknowledgments for reliability, edge quantization for floating-point aggregation using only integer arithmetic, and tail-call chains that create virtual memory beyond eBPF’s 512-byte stack limit. These designs emerge from and exploit the constraints rather than fighting them. AggBox achieves remarkable performance on commodity hardware: 84.5% reduction in broadcast latency, 43× speedup for MapReduce workloads, and 56.1% faster ML gradient aggregation, all without specialized NICs or dedicated cores. Beyond performance, our work demonstrates that constrained environments can drive fundamental innovation in protocol design, offering insights for future resource-limited and verified systems.
Cite as

Jianchang Su, Yifan Zhang, and Wei Zhang. In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 13:1-13:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{su_et_al:OASIcs.NINeS.2026.13,
  author =	{Su, Jianchang and Zhang, Yifan and Zhang, Wei},
  title =	{{In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{13:1--13:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.13},
  URN =		{urn:nbn:de:0030-drops-255981},
  doi =		{10.4230/OASIcs.NINeS.2026.13},
  annote =	{Keywords: eBPF, distributed communication, broadcast, aggregation, in-kernel processing, XDP}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.17
Don’t Get Caught, Keep Your Onions in a Vault

Authors: Humza Ikram, Rumaisa Habib, Muaz Ali, and Zartash Afzal Uzmi

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
When web applications wish to operate anonymously, they routinely host themselves as "Hidden Services" in the Tor network. However, these services are frequently threatened by deanonymization attacks, whereby their IP address and location may be inferred by the authorities. We present VaulTor, a novel architecture for the Tor network that ensures an extra layer of security for the Hidden Services against deanonymization attacks. In this new architecture, a volunteer (vault) is incentivized to host the web application content on behalf of the Hidden Service. The vault runs the hosted application in a Trusted Execution Environment (TEE) and becomes the point of contact for interested clients. This setup can substantially reduce the uptime requirement of the original Hidden Service provider, thereby significantly decreasing the chance of deanonymization attacks against them. Using a vault node in place of the hidden service node does not cause any noticeable performance degradation when accessing the hosted content.
Cite as

Humza Ikram, Rumaisa Habib, Muaz Ali, and Zartash Afzal Uzmi. Don’t Get Caught, Keep Your Onions in a Vault. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 17:1-17:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{ikram_et_al:OASIcs.NINeS.2026.17,
  author =	{Ikram, Humza and Habib, Rumaisa and Ali, Muaz and Uzmi, Zartash Afzal},
  title =	{{Don’t Get Caught, Keep Your Onions in a Vault}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{17:1--17:24},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.17},
  URN =		{urn:nbn:de:0030-drops-256027},
  doi =		{10.4230/OASIcs.NINeS.2026.17},
  annote =	{Keywords: Tor, anonymity, Hidden Services, Trusted Execution Environments}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.18
TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control

Authors: Peter Schafhalter, Alexander Krentsel, Hongbo Wei, Joseph E. Gonzalez, Sylvia Ratnasamy, Scott Shenker, and Ion Stoica

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Autonomous driving system progress has been driven by improvements in machine learning (ML) models, whose computational demands now exceed what edge devices alone can provide. The cloud offers abundant compute, but the network has long been treated as an unreliable bottleneck rather than a co-equal part of the autonomous vehicle control loop. We argue that this separation is no longer tenable: safety-critical autonomy requires co-design of control, models, and network resource allocation itself. We introduce TURBO, a cloud-augmented control framework that addresses this challenge, formulating bandwidth allocation and control pipeline configuration across both the car and cloud as a joint optimization problem. TURBO maximizes benefit to the car while guaranteeing safety in the face of highly variable network conditions. We implement TURBO and evaluate it in both simulation and real-world deployment, showing it can improve average accuracy by up to 15.6%pt over existing on-vehicle-only pipelines. Our code is made available at www.github.com/NetSys/turbo.
Cite as

Peter Schafhalter, Alexander Krentsel, Hongbo Wei, Joseph E. Gonzalez, Sylvia Ratnasamy, Scott Shenker, and Ion Stoica. TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 18:1-18:34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{schafhalter_et_al:OASIcs.NINeS.2026.18,
  author =	{Schafhalter, Peter and Krentsel, Alexander and Wei, Hongbo and Gonzalez, Joseph E. and Ratnasamy, Sylvia and Shenker, Scott and Stoica, Ion},
  title =	{{TURBO: Utility-Aware Bandwidth Allocation for Cloud-Augmented Autonomous Control}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{18:1--18:34},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.18},
  URN =		{urn:nbn:de:0030-drops-256039},
  doi =		{10.4230/OASIcs.NINeS.2026.18},
  annote =	{Keywords: autonomous vehicles, bandwidth allocation, cloud computing, edge computing, machine learning}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.24
SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing

Authors: Siddhant Ray, Xi Jiang, Jack Luo, Nick Feamster, and Junchen Jiang

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Low Latency, Low Loss, and Scalable Throughput (L4S), as an emerging router-queue management technique, has seen steady deployment in the industry. An L4S-enabled router assigns each packet to the queue based on the packet header marking. Currently, L4S employs per-flow queue selection, i.e., all packets of a flow are marked the same way and thus use the same queues, even though each packet is marked separately. However, this may hurt tail latency and latency-sensitive applications because transient congestion and queue buildups may only affect a fraction of packets in a flow. We present SwiftQueue, a new L4S queue-selection strategy in which a sender uses a novel per-packet latency predictor to pinpoint which packets likely have latency spikes or drops. The insight is that many packet-level latency variations result from complex interactions among recent packets at shared router queues. Yet, these intricate packet-level latency patterns are hard to learn efficiently by traditional models. Instead, SwiftQueue uses a custom Transformer, which is well-studied for its expressiveness on sequential patterns, to predict the next packet’s latency based on the latencies of recently received ACKs. Based on the predicted latency of each outgoing packet, SwiftQueue’s sender dynamically marks the L4S packet header to assign packets to potentially different queues, even within the same flow. Using real network traces, we show that SwiftQueue is 45-65% more accurate in predicting latency and its variations than state-of-art methods. Based on its latency prediction, SwiftQueue reduces the tail latency for L4S-enabled flows by 36-45%, compared with the existing L4S queue-selection method.
Cite as

Siddhant Ray, Xi Jiang, Jack Luo, Nick Feamster, and Junchen Jiang. SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 24:1-24:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{ray_et_al:OASIcs.NINeS.2026.24,
  author =	{Ray, Siddhant and Jiang, Xi and Luo, Jack and Feamster, Nick and Jiang, Junchen},
  title =	{{SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{24:1--24:29},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.24},
  URN =		{urn:nbn:de:0030-drops-256093},
  doi =		{10.4230/OASIcs.NINeS.2026.24},
  annote =	{Keywords: Latency prediction, L4S Queue Management}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.27
Making Congestion Control Algorithms Insensitive to Underlying Propagation Delays

Authors: Cyrus Illick, Michael Roger, Vishal Misra, and Dan Rubenstein

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Underlying propagation delays, which significantly impact flow Round-Trip-Times (RTTs), fundamentally shape how congestion control algorithms (CCAs) allocate bandwidth among competing flows. Traditionally, lower RTTs were desirable, as a flow rate would decrease as RTT increased under conventional CCAs such as Reno and Cubic. More recently, algorithms like BBR reversed this behavior, favoring flows with larger RTTs. In modern heterogeneous networks, where competing flows may employ different CCAs and serve diverse application workloads, notions of fairness become ambiguous and RTT’s role further complexifies addressing an already very challenging problem. We offer a simple but initially counter-intuitive remedy to address how all CCAs can remove this inherent sensitivity to propagation delay: have all flows "emulate" the same underlying propagation delay. Unsurprisingly, this idea is often met with a lot of resistance, since it is in many ways counter to many fundamental tenets of networking and congestion control. In this paper, we try to make the case that while the idea is not a silver bullet on its own, it can greatly simplify design and predictability across existing and future congestion control protocols going forward.
Cite as

Cyrus Illick, Michael Roger, Vishal Misra, and Dan Rubenstein. Making Congestion Control Algorithms Insensitive to Underlying Propagation Delays. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 27:1-27:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{illick_et_al:OASIcs.NINeS.2026.27,
  author =	{Illick, Cyrus and Roger, Michael and Misra, Vishal and Rubenstein, Dan},
  title =	{{Making Congestion Control Algorithms Insensitive to Underlying Propagation Delays}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{27:1--27:26},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.27},
  URN =		{urn:nbn:de:0030-drops-256122},
  doi =		{10.4230/OASIcs.NINeS.2026.27},
  annote =	{Keywords: Congestion Control, Propagation Delay}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.23
Who Holds the Steering Wheel? Opacity and Consolidation in CDN Replica Selection

Authors: Rashna Kumar, Fabián E. Bustamante, and Marcel Flores

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Replica selection, the process by which CDNs decide which server delivers content, has become a hidden lever of power and fragility in today’s Internet. Most users, operators, and policymakers remain blind to how these decisions are made, yet they shape latency, resilience, and sovereignty at global scale. DNS resolver centralization further distorts this function, concentrating influence in the hands of a few global actors. We present the first methodology to systematically infer CDN replica selection strategies at global scale, enabling third-party visibility into opaque steering mechanisms. Using RIPE Atlas probes and a geographically distributed set of DNS resolvers, we construct latency fingerprints that distinguish DNS-based, anycast, and regional anycast deployments. We validate our approach on well-documented global providers before applying it to a diverse set of 17 global and regional CDNs serving the top 1,000 websites across 19 countries, covering 66% of Internet users. We also examine ECS support and its interaction with DNS-based redirection. Our findings show that DNS-based steering remains the dominant approach, used by over 70% of CDNs and responsible for most delivered bytes, yet regional variation and mixed strategies complicate the picture. These results highlight replica selection not only as a technical optimization, but as a sociotechnical risk: opaque steering decisions, particularly among regional CDNs, amplify the effects of resolver consolidation and shape the Internet’s future resilience and control.
Cite as

Rashna Kumar, Fabián E. Bustamante, and Marcel Flores. Who Holds the Steering Wheel? Opacity and Consolidation in CDN Replica Selection. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 23:1-23:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kumar_et_al:OASIcs.NINeS.2026.23,
  author =	{Kumar, Rashna and Bustamante, Fabi\'{a}n E. and Flores, Marcel},
  title =	{{Who Holds the Steering Wheel? Opacity and Consolidation in CDN Replica Selection}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{23:1--23:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.23},
  URN =		{urn:nbn:de:0030-drops-256085},
  doi =		{10.4230/OASIcs.NINeS.2026.23},
  annote =	{Keywords: CDN, replica selection, Anycast, Regional Anycast, DNS, consolidation, centralization}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.5
OrbitalBrain: A Distributed Framework for Training ML Models in Space

Authors: Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Earth observation nanosatellites capture high-resolution photos of the Earth in near real-time. These images increasingly support ML applications that are critical for safety and response, such as forest fire and flood detection. However, the downlink bandwidth is limited, resulting in days or weeks of delay from image capture to training. In this work, we propose OrbitalBrain, an efficient in-space distributed ML training framework that leverages limited and predictable satellite compute, bandwidth, and power to intelligently balance data transfer, model aggregation, and local training. Our evaluations demonstrate that OrbitalBrain achieves 1.52×-12.4× speedup in time-to-accuracy while always reaching a higher final model accuracy compared to state-of-the-art ground-based or federated learning baselines. Furthermore, our approach is complementary to satellite imagery capturing and downloading, enhancing the overall efficiency of satellite-based applications.
Cite as

Om Chabra, Chenning Li, Kevin Hsieh, Santiago Segarra, Behnaz Arzani, Peder Olsen, and Ranveer Chandra. OrbitalBrain: A Distributed Framework for Training ML Models in Space. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 5:1-5:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chabra_et_al:OASIcs.NINeS.2026.5,
  author =	{Chabra, Om and Li, Chenning and Hsieh, Kevin and Segarra, Santiago and Arzani, Behnaz and Olsen, Peder and Chandra, Ranveer},
  title =	{{OrbitalBrain: A Distributed Framework for Training ML Models in Space}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{5:1--5:32},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.5},
  URN =		{urn:nbn:de:0030-drops-255907},
  doi =		{10.4230/OASIcs.NINeS.2026.5},
  annote =	{Keywords: Satellite networks, Distributed machine learning, Federated learning, Earth observation, In-orbit computing}
}
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