Abstract
Distributed agent-based simulations often suffer from an imbalance in computational load, leading to a suboptimal use of resources. This happens when part of the computational resoures are waiting idle for another process to finish. Self-adaptive load-balancing algorithms have been developed to use these resources more optimally. These algorithms are typically implemented ad-hoc, making re-usability and maintenance difficult. In this work, we present a generic self-adaptive framework. This methodology is evaluated with the Acsim framework on two simulations: a micro-traffic simulation and a cellular automata simulation. For each of these scenarios a scalable and adaptive load-balancing algorithm is implemented, showing significant improvements in execution time of the simulation.
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Bosmans, S., Bogaerts, T., Casteels, W., Mercelis, S., Denil, J., Hellinckx, P. (2021). Adaptivity in Distributed Agent-Based Simulation: A Generic Load-Balancing Approach. In: Swarup, S., Savarimuthu, B.T.R. (eds) Multi-Agent-Based Simulation XXI. MABS 2020. Lecture Notes in Computer Science(), vol 12316. Springer, Cham. https://doi.org/10.1007/978-3-030-66888-4_1
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DOI: https://doi.org/10.1007/978-3-030-66888-4_1
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