A dependency-aware task offloading in IoT-based edge computing system using an optimized deep learning approach

doi:10.1016/j.parco.2025.103161

2025-10-01

PRE

Shiva Shankar Reddy

N Silpa

Gadiraju Mahesh

V. V. R. Maheswara Rao *

DOI：10.1016/j.parco.2025.103161

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摘要

En 中文

Internet of Things (IoT) devices produce a lot of data, which can be difficult to process on limited computing systems. Edge computing aims to solve this issue by providing localized processing power at the edge of IoT networks to reduce communication delays and network bandwidth. Because of their limited resources and task dependencies, edge computing systems are facing computational issues as a result of the growing usage of IoT devices. An efficient task-offloading system that combines the Fire Hawk Optimizer (FHO) and Deep Reinforcement Learning (DRL) is proposed in this research to address these issues. This paper proposes leveraging deep learning techniques to prioritize and offload computational tasks from IoT applications to edge computing systems, addressing task interdependencies and resource constraints to enhance efficiency. The proposed method consists of two components. The first component uses Petri-Net modelling to analyze interdependencies among tasks, identify subtasks, and map their relationships. The second component uses a residual neural network-based actor-critic deep reinforcement learning (ResNet-ACDRL) decision-making model to offload tasks. Task dependencies and resource availability are assessed by the DRL component, namely a ResNet-ACDRL model, which is utilized to dynamically learn and enhance task-offloading strategies. In order to ensure optimal task allocation across local, edge, and cloud computing resources, the FHO is then used to refine these learned policies. Here, the term policy refers to the strategy used by the system to decide the most suitable resource for task execution. This dual approach strategy drastically reduces energy usage and execution delays. The suggested framework outperforms existing methods, according to experimental data, especially when managing task interdependencies and a variety of computational loads. The proposed method has been shown to significantly improve time delay and energy consumption compared to existing methods.

Keyword:

Task offloading

Optimal offloading in server

Dependency-aware task offloading

Petri-Net model

ResNet

FHO

Identify the interdependencies task

Subtask