Predicting time execution in fog computing using neural network and input evaluation with Interpretive Structural Modeling (ISM-ANN)

Ely Cheick  Maaloum ,Franklin Manene ,Vitalice Oduol

doi:10.24294/jipd11428

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Predicting time execution in fog computing using neural network and input evaluation with Interpretive Structural Modeling (ISM-ANN)

Ely Cheick Maaloum

Franklin Manene

Vitalice Oduol

Journal of Infrastructure Policy and Development 2025, 9(4); https://doi.org/10.24294/jipd11428

Submitted：27 Jan 2025

Accepted：24 Mar 2025

Published：19 Dec 2025

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Abstract

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

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References

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