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Distributed DRL-Based Computation Offloading Scheme for Improving QoE in Edge Computing Environments.

Jinho Park1, Kwangsue Chung1

  • 1Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Republic of Korea.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a distributed deep reinforcement learning (DRL) scheme to enhance the quality of experience (QoE) in edge computing. The novel approach improves task offloading by considering temporal states and experience importance, leading to better rewards.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Edge computing enhances user experience by processing data closer to the source.
  • Reinforcement learning (RL) and deep reinforcement learning (DRL) are used for optimizing edge collaboration.
  • Existing DRL schemes lack temporal state consideration and efficient experience utilization in distributed environments.

Purpose of the Study:

  • To propose a distributed DRL-based computation offloading scheme for improved Quality of Experience (QoE) in edge computing.
  • To address limitations in existing DRL schemes regarding temporal states, experience importance, and data sparsity.

Main Methods:

  • Developed a DRL scheme that models task service time and load balance for offloading target selection.
Keywords:
computation offloadinginterning of thingslinear regressionmobile edge computingreinforcement learning

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  • Incorporated least absolute shrinkage and selection operator (LASSO) regression and an attention layer to account for temporal states.
  • Utilized TD error and critic network loss to learn policies based on experience importance.
  • Implemented adaptive experience sharing between agents using strategy gradients to mitigate data sparsity.

    • Main Results:

    • The proposed scheme demonstrated lower variation in performance compared to existing methods.
    • Achieved higher cumulative rewards, indicating more effective optimization of the offloading policy.
    • Successfully addressed temporal state representation and experience utilization challenges in distributed DRL.

    Conclusions:

    • The novel distributed DRL scheme significantly enhances QoE in edge computing environments.
    • The integration of temporal state awareness and experience prioritization leads to superior learning performance.
    • Adaptive experience sharing effectively resolves data sparsity issues in distributed DRL for computation offloading.