ArticleF1000Research2026
Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence.
Article in F1000Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
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 Conclusions: This approach are evaluated using RMSE, MAE, and R
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