Dynamic Maintenance Cost Optimization in Data Centers: An Availability-Based Approach for K-out-of-N Systems
Bibliographic record
Abstract
Data Centers (DCs) are critical infrastructures that support the digital world, requiring fast and reliable information transmission for sustainability. Ensuring their reliability and efficiency is essential for minimizing risks and maintaining operations. This study presents a novel availability-driven approach to optimizing maintenance costs in DC Uninterruptible Power Supply (UPS) systems configured in a parallel k-out-of-n arrangement. The model integrates reliability and availability metrics into a dynamic optimization framework, determining the optimal number of components needed to achieve the desired availability while minimizing maintenance costs. Through simulations and a case study by utilizing variable failure rates and monthly maintenance costs, the model achieves a combined system availability of 99.991%, which exceeds the Tier 1 DC requirement of 99.671%. A sensitivity analysis, incorporating ±10% variations in Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), and maintenance costs, was conducted to demonstrate the model’s robustness and adaptability across diverse operational conditions. The analysis also evaluates how different k-out-of-n UPS system configurations influence overall availability and maintenance costs. Additionally, feasible k-out-of-n configurations that achieve the required system availability while balancing operational costs were examined. Furthermore, the optimal number of UPS components and their associated minimum costs were compared across different DC tiers, highlighting the impact of varying availability requirements on maintenance strategies. These results showcase the model’s effectiveness in supporting critical maintenance planning, providing DC managers with a robust tool for balancing operational expenses and uptime.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".