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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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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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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.

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A Simulation-Optimisation Tool for Routing Drones in Realistic Conditions

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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Real-time data injection on a dynamic data driven application system for wildland fire spread prediction in grid environments

Real-time data injection on a dynamic data driven applicatio...

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23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009

作者： Rodríguez, Roque Computer Architecture and Operating Systems Department Universitat Autönoma de Barcelona

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Time aware genetic algorithm for forest fire propagation prediction: exploiting multi-core platforms

Time aware genetic algorithm for forest fire propagation pre...

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作者： Artés, Tomàs Cencerrado, Andrés Cortés, Ana Margalef, Tomàs Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Barcelona Spain

Forest fire propagation prediction is a crucial issue when fighting these hazards as efficiently as possible. Several propagation models have been developed and integrated in computer simulators. Such models require a set of input parameters that, in some cases, are difficult to know or even estimate precisely beforehand. Therefore, a calibration technique based on genetic algorithm (GA) was introduced to reduce the uncertainty in input parameters values and improve the accuracy of the predictions. Such a technique requires the execution of a set of simulations and several iterations of the process to calibrate the values of the input parameters. To reduce the execution time of this calibration stage, an Message Passing Interface master/worker scheme was developed to distribute the simulations of one iteration among the worker processes. However, the execution time of each simulation varies drastically depending on the particular input parameters used, provoking a significant load imbalance. To overcome this imbalance and reduce execution time to operational requirements, core allocation policies have been developed. These policies are based on execution time estimation and classification of simulations according to the estimated execution time. Then, multicore capabilities of the current systems are applied to devote more resources (cores) to the longest simulations reducing the load imbalance. These simulations that are estimated as taking too long, even when many resources are devoted to them, require especial consideration. So, a generation time limit has been introduced, and three different strategies have been designed considering individuals that exceed the generation execution time limit. In the first one, the longest individuals are replaced before starting the execution with shorter individuals (Time Aware Core allocation with replacement). In the second one, these individuals are executed, but when the generation limit is reached, the individuals still ex

关键词： Calibration

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Predictive and distributed routing balancing on high-speed cluster networks

Predictive and distributed routing balancing on high-speed c...

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23rd International Symposium on computer architecture and High Performance Computing, SBAC-PAD 2011

作者： Castillo, Carlos Núñez Lugones, Diego Franco, Daniel Luque, Emilio Computer Architecture and Operating Systems Department Universitat Autònoma de Barcelona Spain

ISBN: (纸本)9780769545738

In high performance clusters current parallel application communication needs such as traffic pattern, communication volume, etc., change along time and are difficult to know in advance. Such needs often exceed or do not match available resources causing resource use imbalance, network congestion, throughput reduction and message latency increase, thus degrading the overall system performance. Studies on parallel applications show repetitive behavior that can be characterized by a set of representative phases. This work presents a Predictive and Distributed Routing Balancing (PRDRB) technique, a new method developed to gradually control network congestion, based on paths expansion, traffic distribution, applications pattern repetitiveness and speculative adaptive routing, in order to maintain low latency values. PRDRB monitors messages latencies on routers and logs solutions to congestion, to quickly respond in future similar situations. Traffic congestion experiments were conducted in order to evaluate the performance of the method, and improvements were observed. © 2011 IEEE.

关键词： Traffic congestion

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Non-blocking adaptive cycles: deadlock avoidance for fault-tolerant interconnection networks

Non-blocking adaptive cycles: Deadlock avoidance for fault-t...

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IEEE International Conference on Cluster Computing Workshops

作者： Zarza, Gonzalo Lugones, Diego Franco, Daniel Luque, Emilio Computer Architecture and Operating Systems Department Universitat Autònoma de Barcelona Spain

ISBN: (纸本)9781424483969

The interconnection network communicates and links together the processing units of modern high-performance computing systems. In this context, network faults have an extremely high impact since most routing algorithms were not designed to tolerate faults. Because of this, just a single fault may stall messages in the network, preventing the finalization of applications, or may lead to deadlocked configurations. In this paper we introduce a scalable deadlock avoidance technique specifically designed to deal with large interconnection networks suffering from a large number of dynamic faults. Our method is based on adding one-slot deadlock avoidance buffers and does not require the use of any virtual channels. Additionally, fully-adaptive routing algorithms may be designed on the basis of our proposal. © 2010 IEEE.

关键词： Fault tolerance

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