Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence.
4 affiliations
- 1University of Fallujah, Al-Fallujah, Al Anbar Governorate, Iraq.
- 2INTI International University & Colleges, Nilai, Negeri Sembilan, Malaysia.
- 3Faculty of Data Science and Information Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
- 4University of Diyala, Baqubah, Diyala Governorate, Iraq.
Abstract
Background
The high variability of workloads makes it very difficult for cloud datacenters to efficiently schedule their tasks and resource allocation. The correct forecasting of future resource utilization allows us to proactively scale and implement more sophisticated scheduling policies, which eventually results in has potential to improve resource utilization and fewer failures.Methods
Leveraging only data that is exposed to the scheduler at task dispatch times, this research assesses four supervised learning approaches for estimation of CPU consumption on the Google Cluster Data v3 trace: Linear Regression, Support Vector Regression, Random Forest, and a Neural Network.Result
The randomized forest version gave the optimum regression performances on a held-out test set (MAE ≈ 0.00233, RMSE ≈ 0.00589, R ² ≈ 0.895), while an ordinary hybrid composition of the two best systems performed similarly. Estimating if a task's real CPU consumption surpasses a train-set-derived high utilization threshold which was achieved up to 0.921 accuracy and ≈ 0.844 best F1, chosen via a detailed threshold sweeping as opposed to an individual set cutoff.Conclusions
This approach are evaluated using RMSE, MAE, and R ² for held-out on test set, while the 2nd threshold-based analysis is utilized to appraise the recognition of high-CPU tasks. These results underscore the potential of data driven approach definitely machine and deep learning-based CPU demand predictions as valuable tools for cloud schedulers, improving resource management, and reducing operational costs. Also discuss the feasibility of deploying the proposed solution on distributed platforms such as Spark and Google Cloud and outline future research directions to integrate predictive models with real-time cloud resource management.
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BibTeX
@article{lateef2026intelligent,
title = {{Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence.}},
author = {Huda Mohammed Lateef and Mohammed Abduljawad Al-Shibly and Ahmed Hadi Ali Al-Jumaili and Omar D Madeeh and Mohammed A S Al-Hitawi and Deshinta Arrova Dewi and Mohd Zakree Bin Ahmad Nazri and Shaima Nasir Kadhim},
journal = {F1000Research},
year = {2026},
month = sep,
doi = {10.12688/f1000research.177203.3},
url = {https://europepmc.org/article/MED/42851260},
note = {CC BY},
}
RIS
TY - JOUR
TI - Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence.
AU - Huda Mohammed Lateef
AU - Mohammed Abduljawad Al-Shibly
AU - Ahmed Hadi Ali Al-Jumaili
AU - Omar D Madeeh
AU - Mohammed A S Al-Hitawi
AU - Deshinta Arrova Dewi
AU - Mohd Zakree Bin Ahmad Nazri
AU - Shaima Nasir Kadhim
PY - 2026
DA - 2026/09/30
JO - F1000Research
DO - 10.12688/f1000research.177203.3
UR - https://europepmc.org/article/MED/42851260
AB - Background The high variability of workloads makes it very difficult for cloud datacenters to efficiently schedule their tasks and resource allocation. The correct forecasting of future resource utilization allows us to proactively scale and implement more sophisticated scheduling policies, which eventually results in has potential to improve resource utilization and fewer failures.Methods Leveraging only data that is exposed to the scheduler at task dispatch times, this research assesses four supervised learning approaches for estimation of CPU consumption on the Google Cluster Data v3 trace: Linear Regression, Support Vector Regression, Random Forest, and a Neural Network.Result The randomized forest version gave the optimum regression performances on a held-out test set (MAE ≈ 0.00233, RMSE ≈ 0.00589, R ² ≈ 0.895), while an ordinary hybrid composition of the two best systems performed similarly. Estimating if a task's real CPU consumption surpasses a train-set-derived high utilization threshold which was achieved up to 0.921 accuracy and ≈ 0.844 best F1, chosen via a detailed threshold sweeping as opposed to an individual set cutoff.Conclusions This approach are evaluated using RMSE, MAE, and R ² for held-out on test set, while the 2nd threshold-based analysis is utilized to appraise the recognition of high-CPU tasks. These results underscore the potential of data driven approach definitely machine and deep learning-based CPU demand predictions as valuable tools for cloud schedulers, improving resource management, and reducing operational costs. Also discuss the feasibility of deploying the proposed solution on distributed platforms such as Spark and Google Cloud and outline future research directions to integrate predictive models with real-time cloud resource management.
SN - 2046-1402
ER -