Exploring the use of Deep Reinforcement Learning to allocate tasks in Critical Adaptive Distributed Embedded Systems

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dc.contributor Proenza Arenas, Julián
dc.contributor Ballesteros Varela, Alberto
dc.contributor.author Rotaeche Fernandez de la Riva, Ramon
dc.date 2022
dc.date.accessioned 2023-06-06T10:33:06Z
dc.date.issued 2022-09-16
dc.identifier.uri http://hdl.handle.net/11201/160729
dc.description.abstract [eng] A Critical Adaptive Distributed Embedded System (CADES) is a group of interconnected nodes that must carry out a set of tasks to achieve a common goal, while fulfilling several requirements associated to their critical (e.g. hard realtime requirements) and adaptive nature. In these systems, a key challenge is to solve, in a timely manner, the combinatorial optimization problem involved in finding the best way to allocate the tasks to the available nodes (i.e. the tasks allocation) taking into account aspects such as the computational costs of the tasks and the computational capacity of the nodes. This problem is not trivial and there is no known polynomial time algorithm to find the optimal solution. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems and, in this work, we explore the application of such approaches to the tasks allocation problem in CADESs. We first discuss the potential advantages of using a DRL-based approach over several heuristic-based approaches to allocate tasks in CADESs, and we then demonstrate how a DRLbased approach can achieve similar results to the best performing heuristic in terms of optimality of the allocation, while requiring less time to generate such allocation. ca
dc.format application/pdf
dc.language.iso eng ca
dc.publisher Universitat de les Illes Balears
dc.rights all rights reserved
dc.rights info:eu-repo/semantics/openAccess
dc.subject 004 - Informàtica ca
dc.subject 62 - Enginyeria. Tecnologia ca
dc.subject.other Deep Reinforcement Learning ca
dc.subject.other Distributed Embedded Systems ca
dc.subject.other Combinatorial Optimization ca
dc.subject.other Machine Learning ca
dc.title Exploring the use of Deep Reinforcement Learning to allocate tasks in Critical Adaptive Distributed Embedded Systems ca
dc.type info:eu-repo/semantics/masterThesis ca
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2023-05-08T09:35:26Z
dc.date.embargoEndDate info:eu-repo/date/embargoEnd/2050-01-01
dc.embargo 2050-01-01
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess


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