Bibliographic record
Abstract
Resources, such as processors and network bandwidth, are allocated to application and common services. In long-running systems, it is common to re-allocate these resources, due to changing mission requirements or to changing resource availability. Two patterns of resource allocation are described in this paper: reactive resource allocation, which begins when the need for reallocation arises, and proactive resource allocation, which is planned before the need arises. Reactive resource allocation is widely used in both commercial and United States Department of Defense (DoD) distributed real-time and embedded (DRE) systems. In addition, reactive resource allocation is the focus of a vigorous research and development community. Proactive resource allocation, on the other hand, seems to be employed primarily, if not exclusively, in DRE systems, where in some cases it provides the only means to meet real-time response and recovery requirements. Reactive Resource Allocation The Reactive Resource Allocation design pattern maintains the performance of a computing system within required bounds by observing when the system's performance is close to crossing, or has crossed, a threshold; determining how best to reallocate the available resources to prevent or correct any requirement violation; and then directing that resource reallocation.
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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".