Availability-based maintenance prioritization for data centres: a dynamic programming approach
Bibliographic record
Abstract
Digital infrastructures and Data Centres (DCs) have played a significant role in society for several years in conducting digital transformation. Recently after the global COVID-19 crisis, the importance of DCs has greatly increased because of facing new realities. High demand for digital commerce, online meetings, and other online and cloud services in the digitisation process has given the DC industry special attention. Regarding the critical components of DCs, special requirements, such as minimum availability, reliability, quality, and performance levels, should be addressed in DC operations. Therefore, implementing optimised maintenance management is beneficial for these infrastructures to reduce the risk of failure and ensure a minimum level of reliability and availability while minimising maintenance costs. This paper presents a novel, availability-based model for optimising maintenance prioritisation in DCs. The model leverages a Multiple 0-1 Knapsack problem, solved using Dynamic Programming (DP), to determine the optimal set of components for maintenance actions, considering both DC availability requirements and budget limitations. By incorporating reliability and availability analysis, the DP-based approach generates a maintenance prioritisation plan that maximises DC availability within budgetary constraints. This study provides a practical framework for enhancing maintenance decision-making in DCs, ultimately contributing to more efficient and resilient digital infrastructures.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".