Data center maintenance: applications and future research directions
Bibliographic record
Abstract
Purpose One of the most critical infrastructures is a data center (DC) because of it having many servers, computers and other equipment. DCs provide online services for various companies in the information technology (IT) industry. DC facilities should provide reliable online services while addressing the required quality and performance level considering maximum reliability and availability. The purpose of this study is to represent and classify the main findings in this area and to identify the main research gaps and shortcomings from the perspective of research. Design/methodology/approach This paper provides an organized and systematic literature review focusing on topics regarding the operation and maintenance (O&M) management of DCs. Findings Although there are several studies on O&M management systems for industrial systems and facilities, a limited number of studies with few methods and models have focused on DCs so far and these facilities require more attention. This paper identifies the issues and challenges for DC buildings and facilities and provides a conclusion of the findings to highlight the main research limitations for discovering new potential methods as future research opportunities. Research limitations/implications The paper has highlighted the main practical issues of DCs in terms of maintenance management. Several research works have been discussed specifically for DC’s maintenance, which makes this paper a credible source for researchers, maintenance managers and companies involved in the area of DC. Because several of the reviewed literature were based on real case studies, decision-makers in the DC maintenance sector can take advantage of new research on maintenance scheduling to reduce the costs of maintenance. Originality/value The paper has presented a comprehensive list of frequent keywords in recent publications related to O&M management for DCs. It has provided a categorized list of publications based on by their topic, methodology and case study. Because this paper has discussed research works specifically for DC’s maintenance, it is a credible source for researchers, maintenance managers and companies involved in the area of DCs.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".