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Edge computing driven forest fire spread simulation: An energy-aware study

Journal of Computational Science 2025年 88卷

作者： Carrillo, Carlos Margalef, Tomàs Espinosa, Antonio Cortés, Ana Computer Architecture & Operating Systems department Universitat Autònoma de Barcelona Barcelona Spain

An accurate and fast prediction of forest fire evolution is a crucial issue to minimize its impact. One of the challenges facing forest fire spread simulators is the uncertainty surrounding the input data. While high-performance computing (HPC) platforms help reduce these uncertainties, their accessibility during emergencies is limited due to infrastructure constraints. real time data collection using sensors onboard Unmanned Aerial Vehicles (UAVs) in real time can significantly reduce their uncertainty. However, transmitting this data to HPC environments and returning the results to firefighters remains difficult, especially in areas with poor connectivity. We propose using Edge Computing to address these challenges, leveraging low-consumption GPU-accelerated embedded systems for in situ data processing and wildfire spread simulation. For simulation purposes, the FARSITE forest fire spread simulator has been used. This work aims to demonstrate the feasibility of leveraging Embedded systems with low-consumption GPUs to simulate short-term forest fire spread evolution (up to 5 hours) at high resolution (5 meters). The obtained results highlight that these devices can gather data, simulate the hazard, and deliver prediction results in situ, even without connectivity, opening up the possibility of monitoring and predicting fire behavior at high resolution without employing HPC platforms. (This paper is an extension version of the best poster paper award in ICCS-2024 entitled "From HPC to Edge Computing: A new Paradigm in Forest Fire Spread Simulation".) © 2025 The Authors

关键词： Data reduction

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A biased-randomised iterated local search for the team orienteering arc routing problem allowing different origin and destination

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Journal of Heuristics 2025年第2期31卷 1-27页

作者： Martin, Xabier A. Keenan, Peter Panadero, Javier McGarraghy, Sean Juan, Angel A. Research Center on Production Management and Engineering Universitat Politècnica de València Alcoy Spain Centre for Business Analytics School of Business University College Dublin Belfield Ireland Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Bellaterra Spain

In this paper, we consider the hybridisation of the team orienteering problem and the arc routing problem, the so-called team orienteering arc routing problem (TOARP). This problem has recently raised interest among researchers and practitioners as it can model new routing problems involving unmanned aerial vehicles or other types of electric vehicles. In the TOARP, a fixed fleet of vehicles, initially located at a depot, has to collect as much reward as possible from visiting a set of arcs, while they must visit a set of required arcs. At the same time, they must return to the depot on or before a given deadline (which can be time-based or distance-based). In this paper, we explore an extension of the TOARP in which the origin depot and the destination depot may be different nodes in a network. To solve this version of the problem, we propose a novel biased-randomised iterated local search algorithm. Computational results show the capability, efficiency and robustness of our approach, which provides competitive solutions to the TOARP in short computing times and outperforms some of the best-known solution approaches in the literature. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Local search (optimization)

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Smart Strategies for Project Scheduling: An Adaptive BR-DEH Approach 2nd

Smart Strategies for Project Scheduling: An Adaptive BR-DEH...

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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Martin, Xabier A. Panadero, Javier Juan, Angel A. Research Center on Production Management and Engineering Universitat Politècnica de València Plaza Ferrandiz-Salvador Alcoy03801 Spain Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Carrer de les Sitges Bellaterra08193 Spain

ISBN: (纸本)9783031782404

In recent years, management of multiple complex projects has become increasingly common among large-scale companies, posing significant challenges for company managers. These projects are often identified by numerous tasks that have precedence relations and share a set of limited resources. The main goal of project scheduling is to minimize the total time required to complete these projects. However, this requires careful consideration of resource allocation, careful scheduling choices, and exact sequencing to maximize efficiency while managing the complex interactions between task dependencies. A novel discrete-event heuristic is presented to solve this project scheduling problem, which is later extended into a probabilistic algorithm using biased-randomization techniques. In addition, an adaptive mechanism was introduced to tune the parameters of the algorithm for optimal scheduling in different problem instances. Computational results demonstrate the effectiveness of our approach, finding high-quality solutions for difficult problem sets in short computational times. This intuitive and efficient approach can empower company managers to efficiently improve operational efficiency, reduce project costs, and ultimately increase their business’s success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Scheduling algorithms

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Energy Management of Electric Vehicles: AI-Driven Strategies for Smart Grid-Connected Charging Hubs 2nd

Energy Management of Electric Vehicles: AI-Driven Strategie...

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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Peyman, Mohammad Martin, Xabier A. Panadero, Javier Research Center on Production Management and Engineering Universitat Politècnica de València Plaza Ferrandiz-Salvador Alcoy03801 Spain Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Carrer de les Sitges Bellaterra08193 Spain

ISBN: (纸本)9783031782404

This paper presents an innovative approach to improving electric vehicle (EV) routing in smart cities by combining heuristics and discrete-event simulation, specifically addressing the team orienteering problem. Initially, a biased-randomized heuristic approach is used to discover the most efficient EV routes, but this does not take into account the limitation imposed by the interconnected nature of the power grid. While these routes appear efficient when assessed independently, a more comprehensive evaluation is performed using a simulation model that includes the complex network of interconnected charging stations. This crucial second phase evaluates the practicality of the heuristic solutions in real-world scenarios by investigating how node interconnection affects route efficiency and charging station service. The simulation results provide concrete insights into optimizing EV mobility, enabling route adaptability to the dynamic urban landscape. This approach efficiently bridges the gap between theoretical optimization and actual implementation, emphasizing the need for simulation in verifying and refining routing strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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Optimizing Emergency department Staff Configurations Through Simulation and Montecarlo Clustering Search Algorithm (Mcsa)

SSRN

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SSRN 2025年

作者： Harita, Maria Wong, Alvaro Rexachs, Dolores Bruballa, Eva Epelde, Francisco Luque, Emilio Department of Computer Architecture and Operating Systems Carrer de les Sitges Cerdanyola del Valles Barcelona08193 Spain Computer Science School Avinguda de la Generalitat 202 Sant Cugat del Valles Barcelona08174 Spain Department of Medicine Hospital Universitari Parc Taulí Parc Tauli 1 Sabadell Barcelona08208 Spain

The Hospital Emergency department (ED) provides critical care for acute and urgent conditions, making it one of the most complex areas in healthcare. Optimizing staff configurations to reduce patient waiting times and improve efficiency is a significant challenge. This study introduces a methodology that integrates an agent-based simulation model with the Montecarlo Clustering Search Algorithm (MCSA). Designed to handle multidimensional and combinatorial challenges, the approach identifies optimal staff configurations to manage large patient volumes with varying acuity levels within a ED. Key Performance Indicators (KPIs), such as Length of Stay (LoS), guide the optimization process by aligning medical staff allocation—doctors, nurses, and technicians—with patient arrival patterns. The proposed framework ensures practical, real-world solutions without reallocating personnel from other hospital areas, maintaining operational balance while scaling resources to meet ED demands. A key advantage of MCSA is its ability to reduce computational time compared to exhaustive search methods, enabling efficient exploration of complex search spaces. Results validate this methodology, demonstrating significant reductions in LoS compared to a baseline configuration. By identifying effective staff arrangements, this approach provides decision- makers with a robust and scalable tool to optimize ED operations. It enhances system performance and improves KPIs while avoiding the impracticality of exhaustive computational methods, ensuring applicability in healthcare environments. © 2025, The Authors. All rights reserved.

关键词： Decision making

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A Simulation-Optimisation Tool for Routing Drones in Realistic Conditions 16th

A Simulation-Optimisation Tool for Routing Drones in Realis...

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16th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2024

ISBN: (纸本)9783031873447

The use of drones for routing and monitoring tasks has grown significantly, with applications such as traffic surveillance and road inspections gaining prominence . These real-world scenarios often involve unpredictable factors like fluctuating service times, which add complexity to traditional routing problems. This paper introduces a simulation-optimisation framework for routing drones under realistic conditions . To efficiently solve this problem, we propose a simheuristic approach that integrates a biased-randomised iterated local search metaheuristic with Monte Carlo simulation. Our computational experiments validate the efficiency, robustness, and speed of the proposed method, providing high-quality solutions to routing challenges in uncertain environments. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Heuristic methods

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Cooperating CoScheduling:A Coscheduling Proposal Aimed at Non-Dedicated Heterogeneous NOWs

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Journal of computer Science & Technology 2007年第5期22卷 695-710页

作者： Francesc Giné Francesc Solsona Mauricio Hanzich Porfidio Hernández Emilio Luque Department of Computer Science Universitat de Lleida Spain Department of Computer Architecture & Operating Systems Universitat Autònoma de Barcelona Spain

Implicit coscheduling techniques applied to non-dedicated homogeneous Networks Of Workstations （NOWs） have shown they can perform well when many local users compete with a single parallel job. Implicit coscheduling deals with minimizing the communication waiting time of parallel processes by identifying the processes in need of coscheduling through gathering and analyzing implicit runtime information, basically communication events. Unfortunately, implicit coscheduling techniques do not guarantee the performance of local and parallel jobs, when the number of parallel jobs competing against each other is increased. Thus, a low efficiency use of the idle computational resources is achieved. In order to solve these problems, a new technique, named Cooperating CoScheduling （CCS）, is presented in this work. Unlike traditional implicit coscheduling techniques, under CCS, each node takes its scheduling decisions from the occurrence of local events, basically communication, memory, Input/Output and CPU, together with foreign events received from cooperating nodes. This allows CCS to provide a social contract based on reserving a percentage of CPU and memory resources to ensure the progress of parallel jobs without disturbing the local users, while coscheduling of communicating tasks is ensured. Besides, the CCS algorithm uses status information from the cooperating nodes to balance the resources across the cluster when necessary. Experimental results in a non-dedicated heterogeneous NOW reveal that CCS allows the idle resources to be exploited efficiently, thus obtaining a satisfactory speedup and provoking an overhead that is imperceptible to the local user.

关键词： job scheduling non-dedicated and heterogeneous NOW computing resource allocation

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On/Off-Line Prediction Applied to Job Scheduling on Non-Dedicated NOWs

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Journal of computer Science & Technology 2011年第1期26卷 99-116页

作者： Mauricio Hanzich Porfidio Hernandez Francesc Gine Francesc Solsona Josep L.Lerida Department of Computer Application in Science and Engineering Barcelona Supercomputing CenterBarcelonaSpain Department of Computer Architecture & Operating Systems Autonomous University of BarcelonaBarcelonaSpain Department of Computer Science University of LleidaLleidaSpain

This paper proposes a prediction engine designed for non-dedicated clusters, which is able to estimate the turnaround time for parallel applications, even in the presence of serial workload of the workstation owner. The prediction engine can be configured to work with three different estimation kernels： a Historical kernel, a Simulation kernel based on analytical models and an integration of both, named Hybrid kernel. These estimation proposals were integrated into a scheduling system, named CISNE, which can be executed in an on-line or off-line mode. The accuracy of the proposed estimation methods was evaluated in relation to different job scheduling policies in a real and a simulated cluster environment. In both environments, we observed that the Hybrid system gives the best results because it combines the ability of a simulation engine to capture the dynamism of a non-dedicated environment together with the accuracy of the historical methods to estimate the application runtime considering the state of the resources.

关键词： prediction method non-dedicated cluster cluster computing job scheduling simulation

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A computational approach to TSP performance prediction using data mining

A computational approach to TSP performance prediction using...

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21st International Conference on Advanced Information Networking and ApplicationsWorkshops/Symposia, AINAW'07

作者： Fritzsche, Paula Cecilia Rexachs, Dolores Luque, Emilio Computer Architecture and Operating Systems Department University Autonoma of Barcelona Spain

ISBN: (纸本)0769528473

The increase in the use of parallel distributed architectures in order to solve large-scale scientific problems has generated the need for performance prediction for both deterministic applications and non-deterministic applications. The development of a new prediction methodology to estimate the execution time of a hard data-dependent parallel application that solves the traveling salesman problem (TSP) is the primary target of this study. It consists of two big stages;the execution of the TSP algorithms with different input data in order to collect useful data and the application of a data mining procedure through a KDD process. The approach makes it also possible to evaluate other practical problems that can be formulated as TSP problems. The experimental results are quite promising, the capacity of prediction is greater than 75%. © 2007 IEEE.

关键词： Transmission control protocol

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Extracting knowledge to predict TSP asymptotic time complexity

Extracting knowledge to predict TSP asymptotic time complexi...

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17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007

作者： Fritzsche, Paula Cecilia Rexachs, Dolores Luque, Emilio Computer Architecture and Operating Systems Department University Autonoma Barcelona Spain

ISBN: (纸本)0769530192

Computational science (CS) is often referred to as the third science, complementing both theoretical and laboratory science. In this field, new challenges are continuously arising. It allows doing things that were previously too difficult to do due to the complexity of the mathematics, the large number of calculations involved, or a combination of both. The asymptotic time complexity definition of both deterministic and non-deterministic algorithms to solve all kind of problems is one of the key points in computer science. Knowing the limit of the execution time of an algorithm when the size of the problem goes to infinity is essential. In particular, data-dependent applications is an extremely challenging problem because for a specific issue the input data sets may cause variability in execution times. The development of an entire approach to define the asymptotic time complexity of a hard data-dependent parallel application that solves the traveling salesman problem (TSP) is the focus of this study. This is a process designed to explore data in search of patterns and/or relationships, and then to predict performance order for new data sets by applying historical detected patterns. Two different parallel TSP algorithms are presented. One of these is used to show the usefulness and the profits of the proposed approach, and the other one is used as witness. The experimental results are quite promising. © 2007 IEEE.

关键词： Traveling salesman problem

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