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Interruption-Aware Computation Offloading in the Industrial Internet of Things.

Khoi Anh Bui¹, Myungsik Yoo²

¹Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This study introduces an interruption-aware task offloading framework for the Industrial Internet of Things (IIoT). It reduces service delay by proactively managing edge server availability and task interruptions.

Keywords:

IIoT edge computing multi-agent deep reinforcement learning task offloading

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Industrial Internet of Things (IIoT) devices have limited computational power, necessitating task offloading to edge servers for efficiency.
Edge server overload can cause interruptions, impacting service availability for local devices.
Current interruption mitigation strategies are often reactive, lacking proactive adaptation.

Purpose of the Study:

To develop a proactive task offloading framework for IIoT that accounts for potential edge server interruptions.
To introduce a realistic load-based interruption model for estimating edge server availability.
To enhance computational efficiency and reduce service delay in IIoT networks.

Main Methods:

Developed a load-based interruption model using an exponential function of computational load to estimate server availability.
Employed Multi-Agent Advantage Actor-Critic (MAA2C) for decentralized, adaptive offloading decisions.
Integrated edge availability, resource constraints, device cooperation, and interruption probabilities into decision-making.

Main Results:

The proposed framework effectively reduces average total service delay in IIoT networks.
Optimized the trade-off between system delay and edge server availability.
Demonstrated the framework's ability to handle interruptions and resource limitations.

Conclusions:

The interruption-aware proactive task offloading framework significantly improves IIoT system efficiency.
The MAA2C approach provides effective decentralized decision-making for complex IIoT environments.
The study offers valuable insights into parameter impacts on interruptible edge task offloading.