Optimized Resource Forecasting for Carbon-Intelligent Data Centers With TempoSight: A Hybrid Deep Learning Approach
Bibliographic record
Abstract
Precise resource utilization forecasting is paramount for enabling carbon-intelligent operation in Industrial Internet of Things (IIoT) environments; Cloud data centers (DCs) are no exception. Optimizing their resource allocation not only reduces operational costs but also minimizes energy consumption and associated carbon emissions, contributing to sustainable computing. This paper introducesTempoSight, a novel hybrid Deep Learning (DL) architecture designed for multivariate (MV) time series forecasting, specifically targeting carbon-intelligent resource provisioning in Cloud DCs.TempoSightsynergistically combines the strengths of Patch Time Series Transformers (PatchTST), excelling at capturing global context and long-range dependencies, and Long Short-Term Memory (LSTM) networks, mastering sequential dynamics. To address the critical need for efficient hyperparameter tuning in complex DL models, we propose a Cuckoo Search Algorithm (CSA) based optimization approach. This enables efficient training and optimization ofTempoSightfor MV time series forecasting, leading to improved resource utilization predictions. Rigorous evaluation on the Alibaba and Bitbrains datasets, representing diverse real-world IIoT workload patterns, demonstratesTempoSight’s superior accuracy and robustness compared to state-of-the-art DL models. Notably, under high-load conditions,TempoSightachieves a remarkable 10-15% reduction in Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). This research highlights the critical role of hybrid DL models and intelligent optimization in achieving accurate and efficient resource provisioning, paving the way for carbon-aware IIoT applications and contributing to the broader goals of sustainable and green computing. Simulation studies also suggest thatTempoSight’s high-fidelity CPU forecasts significantly improve the Green Energy Utilization Factor (GEUF) when guiding Predictive Carbon-Aware (PCA) scheduling, quantitatively connecting forecasting precision with concrete sustainability advantages in DC operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".