An economic room-level thermal management of air-cooled cloud data centers based on human brain emotional intelligence
Bibliographic record
Abstract
Cloud Data Centers (CDCs) are consistently recognized as pivotal in the digital economy due to their provision of flexible and cost-effective solutions for storing and processing large volumes of data. These facilities enable businesses to adapt to evolving demands, support advanced technologies, and facilitate global collaboration by offering instant access to resources. Consequently, ensuring their reliable and uninterrupted operation remains a primary concern for technology enterprises and service providers. Among the various factors influencing the performance of CDCs, thermal conditions are of particular significance. In this context, a novel Brain Emotional Learning-Based Intelligent Controller (BELBIC) is proposed in this paper for room-level temperature regulation of an air-cooled CDC. Unlike other established controllers, the proposed BELBIC is both intelligent and adaptive, thereby exhibiting superior performance and adaptability to the dynamic conditions of CDCs. To evaluate the efficacy of the proposed approach, it has been implemented in an air-cooled CDC comprising two server clusters across four distinct weather seasons. The performance of the proposed BELBIC is subsequently benchmarked against four existing controllers, with comparisons based on heat cost. In Cluster 1, Brain Emotional Learning-Based Intelligent Controller (BELBIC) reduces costs by approximately 94.00% to 95.72% compared to Fractional Order Proportional Integral controller (FPI) and Fractional Order Proportional Integral Derivative controller (FPID), and by about 71.13% to 72.47% compared to Proportional Integral Derivative controller (PID) and Proportional Integral controller (PI). Similarly, in Cluster 2, BELBIC achieves cost reductions of around 93.84% to 95.65% versus FPI and FPID, and approximately 70.51% to 72.26% versus PID and PI. The results demonstrate that the proposed model surpasses the others by achieving reduced heat costs and faster thermal regulation, attributed to its intelligent and adaptive features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".