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-...
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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 r...
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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 ...
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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. Initi...
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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...
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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 unpredictab...
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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 ...
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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.
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. T...
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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.
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-determinist...
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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 pre...
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