Ensemble-Based Cloud Workload Prediction Using Recent AI and ML Methods for Optimized Resource Management & Scheduling

Abstract

with the rising demand for efficient cloud computing and resource management, precise workload prediction has become vital. This paper explores altered methods used for workload predicting, from traditional methods to recent machine learning methods. We train models such as XGBoost, LightGBM, CatBoost, LSTM, and GRU, along with an ensemble method, to know their efficiency in practical cloud environments. The study uses the Alibaba Cluster 2017 dataset, focusing on batch (offline) workloads for well prediction precision. Numerous pre-processing methods, with outlier detection, normalization, and sequence creation, are applied to increase model performance. We associate the results of distinct models and ensemble methods using performance parameters like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that whereas deep learning models seizure sequential patterns, ensemble techniques deliver improved complete stability and correctness. This research shows the importance of merging multiple models to improve workload predicting and increase cloud resource consumption.

Introduction

Workload prediction is crucial in cloud computing for effective resource management, latency reduction, and energy efficiency. Traditional statistical and rule-based methods struggle with dynamic workloads, prompting the use of AI and machine learning (ML) techniques. This study explores individual models—XGBoost, LightGBM, CatBoost (gradient boosting ML models), and deep learning models LSTM and GRU—and also proposes an ensemble approach combining these models to improve prediction accuracy.

The paper reviews various AI/ML methods, including supervised learning, unsupervised clustering, reinforcement learning, and hybrid models that integrate optimization algorithms. Deep learning models like LSTM and GRU capture sequential workload patterns effectively, with GRU offering computational efficiency but slightly lower accuracy than LSTM. Ensemble models leverage complementary strengths of individual algorithms, yielding more stable and precise workload forecasts.

The methodology involves training and testing the models on the Alibaba Cluster 2017 dataset, focusing on predicting the number of instances. Data preprocessing includes outlier detection, normalization, and sequential data formatting. The ensemble model combines predictions via weighted averaging.

Results show the ensemble method outperforms individual models in Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), demonstrating enhanced predictive performance. Visualizations compare actual vs. predicted workloads for all models, confirming the effectiveness of AI-driven workload prediction in dynamic cloud environments.

Conclusion

This study discovers the development in workload prediction using AI and machine learning methods, importance their real time applications in cloud surroundings. Compared to old-style methods, deep learning, reinforcement learning, and hybrid AI models demonstrate substantial growths in accuracy and efficiency. The use of progressive models like GRU, CatBoost, LightGBM, and XGBoost—together separately and within an ensemble—reveals the potential for improved workload predicting and cloud resource management. Future research should emphasis on additional benchmarking these models, improving their compliance to real-time workload variations, and mixing energy-efficient policies. Moreover, emerging scalable AI-driven workload prediction structures will be crucial for enhancing modern cloud infrastructures and improving complete cloud system performance.

References

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Copyright

Copyright © 2025 Kirtikumar J. Sharma, Dr. Narendra M. Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.