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Coding-Based Large-Scale Task Assignment for Industrial Edge Intelligence
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DOI：10.1109/TNSE.2019.2942042.png)
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Abstract
En 中文
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.
Keywords:
Task analysis
Servers
Edge computing
Energy consumption
Optimization
Cloud computing
Computational modeling
Industrial edge computing
task assignment
automatic algorithm generation
operator-coding
group optimization
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