Journal of ISMAC
IRO Journals
Volume 6 — Issue 4 — December 2024

Prediction of Workloads in Cloud using ARIMA-ANN

https://doi.org/10.36548/jismac.2024.4.003
Pages: 327-342
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Suriya S.
Associate Professor, Department of Computer Science and Engineering, PSG College of Technology
Surya Arvindh M.
PG Scholar, Department of Computer Science and Engineering, PSG College of Technology
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How to Cite

S., Suriya, and Surya Arvindh M. 2025. “Prediction of Workloads in Cloud Using ARIMA-ANN”. Journal of ISMAC 6 (4): 327-42. https://doi.org/10.36548/jismac.2024.4.003.
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Keywords

— ARIMA
— ANN
— Load Balancing
— Time Series data
— MIT Supercloud
Published: 16-01-2025

Abstract

This study introduces an innovative hybrid ARIMA-ANN model personalized for cloud workload prediction. Unlike existing models that focus solely on linear or nonlinear patterns, the approach combines the strengths of ARIMA for time-series linear trends and ANN for nonlinear data complexities. This integration ensures higher accuracy, as validated using the MIT Supercloud dataset. The methodology leverages data pre-processing, sensitivity analysis, and advanced validation techniques, demonstrating improved accuracy in scenarios of high workload variability. This model supports cloud providers in resource optimization and dynamic load management.

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