Coding-Based Large-Scale Task Assignment for Industrial Edge Intelligence

Coding-Based Large-Scale Task Assignment for Industrial Edge Intelligence
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基于编码的工业边缘智能大规模任务分配

DOI:
10.1109/tnse.2019.2942042
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发表时间:
2019-09
影响因子:
6.6
通讯作者:
Wang Xiaokang
Wang Xiaokang
中科院分区:
计算机科学3区
文献类型:
--
作者:
Ren Lei;Laili Yuanjun;Li Xiang;Wang Xiaokang

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工业边缘计算结合了智能传感器、制造设备、物联网等各种智能设备,最终目标是提供工业边缘智能。分布式边缘服务器如何处理具有多设备连接特性的大规模任务是实现这一目标的主要挑战之一。针对这一问题,本文提出了一种基于遗传编码的群体进化算法,该算法采用基于编码的算子来近似不同类型的进化算子。一个简单的分组策略也被引入到加速优化过程。实验结果表明,该算法能够在很短的时间内为大规模任务提供接近最优的解决方案,与传统的方法相比。
Industrial edge computing, combing various smart devices such as smart sensors, manufacturing equipment, and Internet of Things, has an ultimate goal to provide the industrial edge intelligence. Assigning large-scale tasks with multi-devices connection property for distributed edge servers is one of the main challenges to realize this goal. To address this question, a generative-coding group evolution algorithm, which involves a coding-based operator to approximate different sorts of evolutionary operators, is proposed in this paper. A simple grouping strategy is also introduced to accelerate the optimization process. Experimental results on three cases show that this algorithm is able to provide near-optimal solutions for large-scale tasks within a very short time compared with some traditional approaches.
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