Bibliographic record
Abstract
The environmental impact of Information and Communication Technologies (ICT) continues to grow, driven notably by increasing usage, rebound effects, and emerging demands. However, despite the virtual nature of its services, the sector remains inherently constrained by its materiality and cannot rely on an infinite pool of resources. As a result, the wide variety of supported services may need to be managed under stricter limits within hosting facilities in the future. Contrary to common assumptions, we show that tenants typically do not share computing resources, even in environments commonly perceived as mutualized, such as cloud platforms. Time-sharing has been progressively phased out for reasons of performance, security, predictability, and, perhaps more importantly, due to the decreasing cost of computing resources. This paper advocates for managing fewer physical resources by improving resource sharing between tenants. It represents a paradigm shift, moving beyond traditional time-sharing at the hardware level to a higher abstraction. This approach entails "doing with fewer resources" under conditions of "reduced performance". Nonetheless, enhancing the mutualization of infrastructure can reduce cluster sizes (through consolidation) and improve energy efficiency, with gains related to the accepted performance trade-off, a situation potentially more socially acceptable than eliminating services. We review the current state of the art, identify challenges and opportunities, propose interpretations of Time-Shared Computing, and outline key research directions.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".