Research on Computing Power Resources-Based Clustering Methods for Edge Computing Terminals

doi:10.3390/app152011285

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Research on Computing Power Resources-Based Clustering Methods for Edge Computing Terminals

2025-10-21

DOI：10.3390/app152011285

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Abstract

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In the cloud-edge-end three-tier architecture of edge computing, the cloud, edge layer, and end-device layer collaborate to enable efficient data processing and task allocation. Certain computation-intensive tasks are decomposed into subtasks at the edge layer and assigned to terminal devices for execution. However, existing research has primarily focused on resource scheduling, paying insufficient attention to the specific requirements of tasks for computing and storage resources, as well as to constructing terminal clusters tailored to the needs of different subtasks.This study proposes a multi-objective optimization-based cluster construction method to address this gap, aiming to form matched clusters for each subtask. First, this study integrates the computing and storage resources of nodes into a unified concept termed the computing power resources of terminal nodes. A computing power metric model is then designed to quantitatively evaluate the heterogeneous resources of terminals, deriving a comprehensive computing power value for each node to assess its capability. Building upon this model, this study introduces an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) clustering algorithm. This algorithm incorporates simulated annealing and adaptive genetic operations to generate the initial population and employs a differential mutation strategy in place of traditional methods, thereby enhancing optimization efficiency and solution diversity. The experimental results demonstrate that the proposed algorithm consistently outperformed the optimal baseline algorithm across most scenarios, achieving average improvements of 18.07%, 7.82%, 15.25%, and 10% across the four optimization objectives, respectively. A comprehensive comparative analysis against multiple benchmark algorithms further confirms the marked competitiveness of the method in multi-objective optimization. This approach enables more efficient construction of terminal clusters adapted to subtask requirements, thereby validating its efficacy and superior performance.

Keywords:

computing power resources

edge computing

clustering

NSGA-III alogrithm

multi-objective optimization