An Event-Driven System Architecture for a 15-Minute Urban Flood Digital Twin: A Hanoi Case Study

Mai Anh Tran; Vy Nguyen; Vova Nguyen

Home/Archive/arXiv

arXiv · 2610.25917

An Event-Driven System Architecture for a 15-Minute Urban Flood Digital Twin: A Hanoi Case Study

Mai Anh Tran·Vy Nguyen·Vova Nguyen

View original source ↗Read source document ↗Export metadata ↓

Abstract

Flash floods in Hanoi submerge roads within minutes of heavy rain onset, yet public flood advisories remain retrospective. Hydrodynamic models could supply forward-looking depth guidance, but they are typically run offline for historical events, and prior studies have not evaluated whether physics-based simulation, spatial serving, and vehicle-aware routing can operate together within a fixed refresh cycle using a cloud solver and a single commodity server. This paper evaluates an end-to-end prototype of an automated flood digital twin designed to meet a 15-minute refresh cycle under data scarcity. The architecture organizes execution into decoupled, event-driven workflows that chain weather-gated 3Di simulations through hydraulic restart states, serve flood depths through a dual-track spatial layer, and publish road-edge depths via standardized output contracts. Evaluated on a 57-run storm replay in the My Dinh and Tay Mo sector of Hanoi driven by satellite rainfall estimates, every cycle completed within the 15-minute deadline, publishing a ready edge table at a median of 537 s after slot start. Indexed raster tiles resolved point-depth lookups in a median of 0.54 ms (277 times faster than un-tiled rasters), while pre-rendered vector tiles served web maps without database queries. These findings demonstrate that a commodity server coupled to a cloud solver can sustain the 15-minute operational cycle and decouple routing policies from simulation internals. Because simulated depths were not validated against field gauges and the replay used observed satellite rainfall, the results establish system feasibility rather than forecast accuracy or route safety.

Explore related subjects

cs.DC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mai Anh Tran, Vy Nguyen, Vova Nguyen. 2026-10-19. An Event-Driven System Architecture for a 15-Minute Urban Flood Digital Twin: A Hanoi Case Study. https://arxiv.org/abs/2610.25917

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DIFFLET: A Fast Inference Engine for Diffusion Transformer Models on AWS Trainium

On AWS Trainium2, every component of a diffusion model runs as a program compiled ahead of time for fixed shapes, while pipelines differ across models, prompt lengths across requests, and caching decisions across denoising steps. We present Difflet, a serving engine in which five model pipelines share one compilation backend, joint attention is split across cores by attention head, and a host controller decides at each denoising step whether to run the denoiser or reuse its earlier output. A small probe program computes the caching signal on the device and returns one number per step, and a resident model process keeps every program loaded across requests. Across five image and video model families, fused attention accelerates denoiser steps by 1.2-4.3x over the same framework using PyTorch/XLA attention, and at 32.8k visual tokens it is the only attention that compiles. On four of the five models, resident serving is 1.5-3.4x faster than standalone runs that reload weights from a warm file cache for each generation.

cs.DC↗

arXiv2026

Constrained Federated Reinforcement Learning for Secure Offloading in Vehicular Edge Computing

Vehicular Edge Computing (VEC) reduces latency for computation-intensive vehicular applications by offloading tasks to roadside edge servers. Real vehicular workloads, however, consist of dependent subtasks with stringent reliability needs, while the cross-region collaborative learning used to adapt offloading policies must stay robust to heterogeneous data and secure over open wireless channels. We propose CRAFT, a Constrained Reinforcement-based Adaptive Federated Task Offloading framework that casts Directed Acyclic Graph (DAG)-structured offloading and resource allocation as a Constrained Markov Decision Process. CRAFT couples centralized-critic policy learning with Lagrangian relaxation to explicitly regulate the long-term task-completion constraint, and uses two-tier federated aggregation with proximal regularization to jointly mitigate non-Independent and Identically Distributed (non-IID) drift and stabilize constraint adaptation. Confidentiality and integrity for federated parameter exchange are provided by post-quantum homomorphic encryption and lattice-based signatures with modest amortized overhead. On a Virtual Machine-based testbed over a realistic Melbourne Central Business District network with real computation kernels, CRAFT cuts weighted cost by up to 42% over state-of-the-art baselines, maintains a completion rate of around 96%, and adds 6.4% security overhead.

cs.DC↗

arXiv2026

Evaluating Runtime Prediction and Conservative Walltime Policies in HPC Systems

Efficient use of large-scale supercomputing systems depends on accurate runtime estimation and walltime selection. To avoid premature termination, users often overestimate walltime, leading to poor resource utilization and reduced scheduling efficiency. This paper presents a predictor-agnostic framework for safe walltime recommendation in production HPC environments. Rather than proposing a new runtime prediction model, the primary contribution is the SAFE decision layer, which converts predicted runtimes into conservative bounded walltime recommendations suitable for deployment. Runtime prediction is therefore treated as a modular component within the proposed framework. We evaluate the approach using large-scale production job histories from the Fugaku and NREL Eagle supercomputers, comprising approximately 24M and 11M jobs, respectively. Runtime prediction performance is first evaluated using representative predictors, including linear regression, neural networks, and a neural ensemble. The proposed SAFE framework is then evaluated using the NN ensemble predictor. Experimental results indicate that similarity-aware prediction substantially improves runtime estimation for workloads similar to previous executions. When combined with the proposed conservative walltime recommendation policies, these improvements enable walltime gap reductions of approximately 20 - 26% while maintaining job termination rates below 1% across both systems.

cs.DC↗