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Optimized task offloading and resource allocation framework for edge-assisted IoT applications
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DOI：10.1007/s10586-026-06442-w.png)
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Abstract
En 中文
The swift evolution of edge computing has transformed the distributed computing paradigm. Edge computing brings the computation and storage to the network edge, facilitating real-time processing for resource-intensive applications. However, it is challenging to offload the task to the diverse computational layers due to the dynamic and multifarious nature of the edge network. Therefore, our proposed work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications. The proposed work exploits the exploration through the steering hunting mechanism of DLO while exploitation by SCO’s capability of refined local search that yields a balanced exploration-exploitation trade-off leads to enhance the efficacy of Quality of Service (QoS) parameters. The performance of proposed work is assessed against the benchmark algorithm using key performance metrics, including delay, cost, and energy consumption. The experimental result confirms that the proposed work diminishes the delay by up to 24.05%, energy consumption by up to 29.30%, and cost by up to 31.59% compared to the prevailing approaches.
Keywords:
Edge computing
Metaheuristic
Offloading
Resource allocation
Delay
Journal
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4.1
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4.8K
Citations:
7.5K
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