ALTM: Adaptive learning-based thermal model for temperature predictions in data centers
Bibliographic record
Abstract
To design effective control schemes for energy efficiency in data centers, it is crucial to have a thermal model of the system. Constructing thermal models of data centers for temperature prediction is extremely challenging, due to inherent complexity. Computational fluid dynamics (CFD) simulations or physical heat transfer equations are conventionally used to construct such thermal models. More recent approaches combine physical heat transfer rules and data-driven methods in an effort to obtain more accurate models. Our proposed adaptive learning-based thermal model (ALTM) is fast, adapts to thermal changes in the data center environment, and does not require prior knowledge of heat transfer rules between data center entities. Unlike other methods, ALTM is a holistic thermal model that predicts temperature of critical zones using data center operational variables as inputs. The operational variables are the controllable parameters and easily obtained measurements from IT and cooling units. A key use case for ALTM is that it can be effectively used for thermal-aware workload schedulers or cooling system controllers. Our results confirm the accuracy and adaptability of the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".