Development of Tiny Machine Learning Models for Optimal Distribution of Workloads at Edge Networks

Document Type : Original Article

Authors

Computer Engineering Department, Engineering Faculty, Bu-Ali Sina University, Hamedan, Iran.

Abstract

The number of devices connected to the Internet of Things has been expanded rapidly. This issue has caused a significant increase in the computational load in the networks. To overcome this challenge, cloud computing was presented as a suitable solution. However, cloud computing suffers significant delay to process workloads. Processing workloads at the edge of the network and locally leads to reduced latency. But due to the limitation of computing resources at the edge, managing and optimizing resources is considered one of the main challenges. Therefore, in addition to distributing the workloads at the edge of the network and maintaining the balance between energy consumption and delay, the limitation of resources such as memory consumption should be considered. In this paper, an online method based on XCS learning classifier systems (LCS), named TinyXCS, and an offline method based on decision tree, named TinyDT, are proposed to balance energy consumption and reduce delay in processing workloads considering the memory limitation at edge. The experimental results show the superiority of TinyXCS and TinyDT over similar methods. The simulation shows that in addition to workload distribution, the proposed methods can simultaneously reduce delay and energy consumption and create a compromise between them and memory consumption.

Keywords

References

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TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 54, Issue 4 - Serial Number 110
December 2024
Pages 403-412

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