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Speeding Task Allocation Search for Reconfigurations in Adaptive Distributed Embedded Systems Using Deep

Ramón Rotaeche1, Alberto Ballesteros1, Julián Proenza1

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Summary
This summary is machine-generated.

Deep Reinforcement Learning (DRL) offers an efficient solution for task allocation in Critical Adaptive Distributed Embedded Systems (CADES). This approach achieves optimal task allocation comparable to heuristics but in significantly less time.

Keywords:
Deep Reinforcement LearningDistributed Embedded SystemsMachine Learningcombinatorial optimization

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Area of Science:

  • Computer Science
  • Embedded Systems
  • Artificial Intelligence

Background:

  • Critical Adaptive Distributed Embedded Systems (CADES) require efficient task allocation to meet real-time demands.
  • The task allocation problem in CADES is a complex combinatorial optimization challenge.
  • Existing heuristic approaches may not always provide optimal solutions in a timely manner.

Purpose of the Study:

  • To explore the application of Deep Reinforcement Learning (DRL) for task allocation in CADES.
  • To compare the effectiveness of DRL-based task allocation against traditional heuristic methods.
  • To evaluate the trade-offs between allocation optimality and generation time.

Main Methods:

  • Investigated Deep Reinforcement Learning (DRL) algorithms for solving the combinatorial optimization problem of task allocation.
  • Developed and applied a DRL-based approach to allocate tasks across interconnected nodes in a CADES.
  • Benchmarked the DRL approach against established heuristic-based task allocation strategies.

Main Results:

  • The DRL-based approach demonstrated the potential for significant advantages over heuristic methods in CADES task allocation.
  • DRL achieved allocation optimality comparable to the best-performing heuristics.
  • The DRL approach generated task allocations in substantially less time compared to heuristics.

Conclusions:

  • Deep Reinforcement Learning presents a viable and efficient alternative for task allocation in Critical Adaptive Distributed Embedded Systems.
  • DRL offers a promising direction for optimizing resource management in complex embedded systems.
  • The study highlights DRL's capability to balance solution quality with computational efficiency in real-time systems.