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Analysis of Deep Reinforcement Learning Algorithms for Task Offloading and Resource Allocation in Fog Computing

Endris Mohammed Ali1, Jemal Abawajy2, Frezewd Lemma1

  • 1Department of Computer Science and Engineering, College of Electrical Engineering and Computing, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

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This summary is machine-generated.

Deep reinforcement learning (DRL) offers adaptive solutions for task offloading in fog computing environments. This survey provides a comprehensive analysis of DRL applications for optimizing resource allocation and meeting Quality of Service (QoS) requirements in Internet of Things (IoT) systems.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Fog computing is preferred for Internet of Things (IoT) task processing due to resource limitations in edge devices.
  • Task offloading and resource allocation in dynamic fog environments present significant challenges, particularly for meeting Quality of Service (QoS) requirements.

Purpose of the Study:

  • To present a comprehensive survey on the application of Deep Reinforcement Learning (DRL) for task offloading in multi-device, multi-node fog computing environments.
  • To address the gap in existing literature by focusing on full-scale DRL applications beyond traditional centralized offloading.

Main Methods:

  • Systematic analysis and classification of existing literature based on architecture, resource allocation, QoS objectives, offloading topology, optimization strategies, DRL techniques, and application scenarios.
Keywords:
QoSdeep reinforcement learningfog computingresource allocationtask offloading

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  • Development of a taxonomy for DRL-based task offloading models.

    • Main Results:

    • Identification of key challenges, open issues, and future research directions in DRL-based task offloading for fog computing.
    • Classification of DRL approaches across various dimensions of the task offloading problem.

    Conclusions:

    • DRL is a promising approach for adaptive, real-time decision-making in complex fog computing task offloading scenarios.
    • The survey provides valuable insights for researchers and practitioners in developing efficient, scalable, and QoS-aware fog computing applications.