Adaptive Feature Selection and Long-Range Temporal Learning for Multivariate Cloud Workload Prediction
Keywords:
Cloud Workload Prediction, Bacterial Foraging Optimization, Feature Selection, Long Short-Term Memory Networks, Time Series Prediction, Resource Demand Estimation.
Abstract
Workload prediction is essential for efficient cloud resource scheduling, enabling early provisioning, minimized latency, and consistent service quality under variable demand. Accurate prediction is hindered by abrupt load changes, seasonal shifts, and complex multivariate relationships. Many existing models face high-dimensional feature overhead, limited adaptability, or overfitting when applied to volatile workloads. This study introduces a hybrid framework integrating Bacterial Foraging Optimization for optimal feature subset selection with a Long Short-Term Memory network to model long-range temporal dependencies. Experiments on a benchmark cloud workload dataset show that the proposed model achieves an RMSE of 0.142 and an MAE of 0.097, outperforming the best baseline by 8.39% in accuracy and reducing computational time by 22.99%. The findings highlight the model’s capability to deliver precise predictions while lowering operational complexity, offering a balanced solution for accuracy and efficiency in dynamic cloud environments.
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23. https://data.mendeley.com/datasets/b7bp6xhrcd/1
2. Sai Dikshit Pasham, “Dynamic Resource Provisioning in Cloud Environments Using Predictive Analytics,” The Computertech, vol.4, no.2, pp. 1-28, 2018.
3. Shobhana Kashyap, Avtar Singh, “Prediction-based scheduling techniques for cloud data center’s workload: a systematic review,” Cluster Computing, vol. 26, pp. 3209–3235, 2023.
4. Hajer Toumi, Zaki Brahmi, Mohhamed Mohsen Gammoudi, “RTSLPS: Real time server load prediction system for the ever-changing cloud computing environment,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 2, pp. 342-353, 2022.
5. Yao Lu; Lu Liu; John Panneerselvam; Xiaojun Zhai; Xiang Sun; Nick Antonopoulos, “Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters,” IEEE Transactions on Sustainable Computing, vol. 5, no. 3, pp. 308-318, 2020.
6. Lata J. Gadhavi, Madhuri D. Bhavsar, “Adaptive cloud resource management through workload prediction,” Energy Systems, vol. 13, pp. 601–623, 2022.
7. Jitendra Kumar, Ashutosh Kumar Singh, “Cloud datacenter workload estimation using error preventive time series forecasting models,” Cluster Computing, vol. 23, pp. 1363-1379, 2020.
8. Qiong Sun, Zhiyong Tan, Xiaolu Zhou, “Workload prediction of cloud computing based on SVM and BP neural networks,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 39, no. 3, pp. 2861-2867, 2020.
9. Wei Zhong, Yi Zhuang, Jian Sun, Jingjing Gu, “A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine,” Applied Intelligence, vol. 48, pp. 4072–4083, 2018.
10. Deepika Saxena; Jitendra Kumar; Ashutosh Kumar Singh; Stefan Schmid, “Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 4, pp. 1313-1330, 2023.
11. Mohammad Masdari, Afsane Khoshnevis, “A survey and classification of the workload forecasting methods in cloud computing,” Cluster Computing, vol. 23, pp. 2399–2424, 2020.
12. Minxian Xu, Chenghao Song, Huaming Wu, Sukhpal Singh Gill, Kejiang Ye, Chengzhong Xu, “esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments,” ACM Transactions on Internet Technology, vol. 22, no. 3, pp. 1-24, 2022.
13. Fan-Hsun Tseng; Xiaofei Wang; Li-Der Chou; Han-Chieh Chao; Victor C. M. Leung, “Dynamic Resource Prediction and Allocation for Cloud Data Center Using the Multiobjective Genetic Algorithm,” IEEE Systems Journal, vol. 12, no. 2, pp. 1688-1699, 2018.
14. Boyun Liu, Jingjing Guo, Chunlin Li, Youlong Luo, “Workload forecasting based elastic resource management in edge cloud,” Computers & Industrial Engineering, vol. 139, pp. 1-16, 2020.
15. Jitendra Kumar, Ashutosh Kumar Singh, Rajkumar Buyya, “Self-directed learning-based workload forecasting model for cloud resource management,” Information Sciences, vol. 543, pp. 345-366, 2021.
16. Durga S, Esther Daniel, Deepakanmani S, Reshma V.K, “Deep learning-based workload prediction and resource provisioning for mobile edge-cloud computing in healthcare applications,” Sustainable Computing: Informatics and Systems, vol. 47, 2025.
17. Oumaima Ghandour, Said El Kafhali, Mohamed Hanini, “Adaptive workload management in cloud computing for service level agreements compliance and resource optimization,” Computers and Electrical Engineering, vol. 120, 2024.
18. Yashwant Singh Patel, Jatin Bedi, “MAG-D: A multivariate attention network-based approach for cloud workload forecasting,” Future Generation Computer Systems, vol.142, pp. 376-392, 2023.
19. Siyuan Liu, Qian He, Yiting Chen, Fan Zhang, “Wavelet and VMD enhanced traffic forecasting and scheduling method for edge cloud networks,” Computers and Electrical Engineering, vol. 121, pp. 1-18, 2025.
20. Tahseen Khan, Wenhong Tian, Shashikant Ilager, Rajkumar Buyya, “Workload forecasting and energy state estimation in cloud data centres: ML-centric approach,” Future Generation Computer Systems, vol. 128, pp. 320-332, 2022.
21. Sima Jeddi, Saeed Sharifian, “A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing,” Applied Soft Computing, vol. 88, pp.1-16, 2020.
22. Weiping Zheng; Zongxiao Chen; Kaiyuan Zheng; Weijian Zheng; Yiqi Chen; Xiaomao Fan, “WorkloadDiff: Conditional Denoising Diffusion Probabilistic Models for Cloud Workload Prediction,” IEEE Transactions on Cloud Computing, vol. 12, no. 4, pp. 1291-1304, 2024.
23. https://data.mendeley.com/datasets/b7bp6xhrcd/1
How to Cite
Hari Krishnan Andi, Divya, & Hishamuddin Bin M.Salleh. (1). Adaptive Feature Selection and Long-Range Temporal Learning for Multivariate Cloud Workload Prediction. Revista Electronica De Veterinaria, 25(1), 4175-4184. https://doi.org/10.69980/redvet.v25i1.2147
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