Incentive Temperature Control for Green Colocation Data Centers via Reinforcement Learning
Bibliographic record
Abstract
Increasing supply air temperatures is a rule-of-thumb approach to reduce cooling energy usage of data centers (DCs). However, colocation DCs are short of incentive programs to move tenants from the current over-cooling strategy despite the expanding allowable temperature ranges of the computing equipment. This paper considers an essential incentive mechanism, in which the DC operator offers monetary incentives to offset tenants’ electricity payments. We propose an encoder-embedded multi-agent reinforcement learning solution to let the operator agent and tenant agents collaboratively find their policies for deciding the incentives and supply air temperatures, respectively, which are coupled in determining the DC’s total cooling power usage. The solution does not require the cooling power model, which is complex and in general unavailable in practice. Moreover, as each tenant agent learns in the other tenants’ latent state spaces defined by their pre-trained variational autoencoders, only encoded tenants’ states are exchanged, thereby mitigating information leakage concerns. Extensive trace-driven evaluation and comparison with three baselines show that our solution effectively incentivizes tenants to move from the over-cooling strategy and achieves substantial cooling power savings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".