Bandwidth and Computing Resources Provisioning for Grid Applications and Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Applications using grid computing infrastructure usually require resources allocation to satisfy their quality of service (QoS) requirements. Given that the grid infrastructure is a set of computing resources geographically distributed, the support of grid applications requires the allocation of computing resources and bandwidth to enable communication among these resources. The objective is to accommodate as many applications as possible while still satisfying their requirements. Ideally, we would like to accommodate a given Grid application using a set of computing resources (e.g., one server) that are not geographically distributed (e.g., in the same LAN); however, this is not always possible. Indeed, to increase the probability of accommodating grid applications, we may need to use computing resources scattered all over the network; in this case, bandwidth allocation is required to enable communication among these resources. In this paper, we propose an optimization model that enables the "simultaneous" allocation of computing resources and bandwidth for grid application while maximizing the number of grid applications being accommodated. A heuristic is proposed to solve the model with an acceptable response time; simulations show that the proposed approach outperforms existing classical approaches.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it