.png){kind=icon}
返回
Research on Computing Power Resources-Based Clustering Methods for Edge Computing Terminals
.png){kind=icon}
OA
AI
DOI：10.3390/app152011285.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
computing power resources
edge computing
clustering
NSGA-III alogrithm
multi-objective optimization
期刊
A
IF:
2.5
论文数:
7.6K
被引数:
4
机构
引用论文
Incentive-Driven Task Allocation for Collaborative Edge Computing in Industrial Internet of Things激励驱动的工业物联网协同边缘计算任务分配
PREAI
分享收藏Learning-Driven Wireless Communications, towards 6G.png)
2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
OAAI
2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)IF0
OAAI分享收藏Optimization of sensor selection problem in IoT systems using opposition-based learning in many-objective evolutionary algorithms在多目标进化算法中使用基于反向学习的物联网系统中优化传感器选择问题
PREAI
分享收藏Metaheuristic-based task scheduling for latency-sensitive IoT applications in edge computing边缘计算中延迟敏感物联网应用的基于元启发式的任务调度
PREAI
分享收藏Dynamic clustering and routing using multi‐objective particle swarm optimization with Levy distribution for wireless sensor networks基于莱维分布的多目标粒子群优化算法在无线传感器网络中的动态聚类与路由
PREAI
分享收藏
查看更多内容