3 Search Results for "Xu, Yixuan Even"

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.NG-RES.2026.5
SEKHMET: Hash-Chained Perception Contracts for Heterogeneous Real-Time Edge Clusters

Authors: Mohamed El-Hadedy

Published in: OASIcs, Volume 140, 7th Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2026)

Abstract
Real-time perception pipelines on edge clusters are often scheduled as ordinary latency-sensitive pods, even when safety depends on sustained throughput and stable model outputs. This paper presents SEKHMET (Scheduling Edge Kubernetes with Hash-chained Monitoring of End-to-end Telemetry), a perception-aware orchestration layer for lightweight Kubernetes (K3s) clusters that exports window-level perception status as a control-plane signal. SEKHMET evaluates a perception-integrity contract (PIC) once per fixed-duration window and commits each window outcome into a hash-chained perception root that is published to an otherwise unmodified K3s control plane. The prototype uses a Raspberry Pi 5 perception-root node with a Hailo-8L accelerator, USB camera, and GPS receiver running a YOLOv8s detector, while up to five additional nodes generate elastic interference via swarm-stress. Under contract-unaware scheduling with multi-node interference, the end-to-end perception loop delivers ∼0.8-2.2 FPS and violates the PIC timing requirement in most of 214 windows, despite apparently healthy CPU and memory metrics. Under the same and heavier interference, SEKHMET sustains 27-30 FPS with contract_ok = True across 400 protected windows while publishing one 96-byte record per T=5s window (19.2 B/s control-plane payload). These results show that making perception requirements control-plane-visible can turn fragile best-effort perception into a protected cluster-level resource on commodity edge hardware.
Cite as

Mohamed El-Hadedy. SEKHMET: Hash-Chained Perception Contracts for Heterogeneous Real-Time Edge Clusters. In 7th Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2026). Open Access Series in Informatics (OASIcs), Volume 140, pp. 5:1-5:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{elhadedy:OASIcs.NG-RES.2026.5,
  author =	{El-Hadedy, Mohamed},
  title =	{{SEKHMET: Hash-Chained Perception Contracts for Heterogeneous Real-Time Edge Clusters}},
  booktitle =	{7th Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2026)},
  pages =	{5:1--5:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-415-4},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{140},
  editor =	{Ali, Hazem Ismail and Kurunathan, Harrison},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2026.5},
  URN =		{urn:nbn:de:0030-drops-254239},
  doi =		{10.4230/OASIcs.NG-RES.2026.5},
  annote =	{Keywords: edge clusters, K3s, Kubernetes, real-time perception, scheduling, integrity contracts, hash chaining, Hailo-8L}
}
Document
DOI: 10.4230/LIPIcs.ESA.2023.80
On the Perturbation Function of Ranking and Balance for Weighted Online Bipartite Matching

Authors: Jingxun Liang, Zhihao Gavin Tang, Yixuan Even Xu, Yuhao Zhang, and Renfei Zhou

Published in: LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023)

Abstract
Ranking and Balance are arguably the two most important algorithms in the online matching literature. They achieve the same optimal competitive ratio of 1-1/e for the integral version and fractional version of online bipartite matching by Karp, Vazirani, and Vazirani (STOC 1990) respectively. The two algorithms have been generalized to weighted online bipartite matching problems, including vertex-weighted online bipartite matching and AdWords, by utilizing a perturbation function. The canonical choice of the perturbation function is f(x) = 1-e^{x-1} as it leads to the optimal competitive ratio of 1-1/e in both settings. We advance the understanding of the weighted generalizations of Ranking and Balance in this paper, with a focus on studying the effect of different perturbation functions. First, we prove that the canonical perturbation function is the unique optimal perturbation function for vertex-weighted online bipartite matching. In stark contrast, all perturbation functions achieve the optimal competitive ratio of 1-1/e in the unweighted setting. Second, we prove that the generalization of Ranking to AdWords with unknown budgets using the canonical perturbation function is at most 0.624 competitive, refuting a conjecture of Vazirani (2021). More generally, as an application of the first result, we prove that no perturbation function leads to the prominent competitive ratio of 1-1/e by establishing an upper bound of 1-1/e-0.0003. Finally, we propose the online budget-additive welfare maximization problem that is intermediate between AdWords and AdWords with unknown budgets, and we design an optimal 1-1/e competitive algorithm by generalizing Balance.
Cite as

Jingxun Liang, Zhihao Gavin Tang, Yixuan Even Xu, Yuhao Zhang, and Renfei Zhou. On the Perturbation Function of Ranking and Balance for Weighted Online Bipartite Matching. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 80:1-80:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{liang_et_al:LIPIcs.ESA.2023.80,
  author =	{Liang, Jingxun and Tang, Zhihao Gavin and Xu, Yixuan Even and Zhang, Yuhao and Zhou, Renfei},
  title =	{{On the Perturbation Function of Ranking and Balance for Weighted Online Bipartite Matching}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{80:1--80:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.80},
  URN =		{urn:nbn:de:0030-drops-187334},
  doi =		{10.4230/LIPIcs.ESA.2023.80},
  annote =	{Keywords: Online Matching, AdWords, Ranking, Water-Filling}
}
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