An efficient mechanism to determine a function for load distribution in fog computing based on the use of learning classification systems

Document Type : Original Article

Authors

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

Abstract

In recent years, the Internet of Things is one of the most popular technologies that facilitate new interactions between people and humans to increase the quality of life. With the rapid development of the Internet of Things, the fog computing model is emerging as an attractive solution for data processing of Internet of Things applications. In the fog environment, IoT applications are run by intermediate computing nodes in the fog as well as physical servers in cloud data centers. On the other hand, due to resource limitations, resource heterogeneity, dynamic nature and lack of energy, it is necessary to consider resource management and energy management issues as one of the challenging problems in fog computing. Recently, some researches have been done to create a balance between energy and cost in fog computing. In this research, while examining these approaches, an efficient method for approximating the load distribution function with two methods based on batch learning systems called XCSF and BCM-XCSF in fog processing nodes in order to optimize the previous approaches as much as possible and manage fog processing resources. These two methods differ in having a memory to store the best classifiers. Experiments indicate that these two methods, like XCS and BCM-XCS, have a suitable load distribution. These two methods, especially the BCM-XCSF method, in addition to reducing the computational overhead; It reduces the delay by about 60% and optimizes energy consumption.

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References

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Applied and basic Machine intelligence research
Volume 2, Issue 1
September 2024
Pages 1-13

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