A Framework to Achieve Guaranteed QoS for Applications and High System Performance in Multi-Institutional Grid Computing
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
Providing QoS guarantees to the applications in a multi-institutional grid is a challenging task. Although advance reservations (ARs) can provide QoS guarantees for the applications, they often seriously degrade system performance for resource owners. In this paper, we present a framework for AR based resource sharing that not only provides QoS guarantees for the applications but also ensures high utilization of the resources. This paper focuses on the application-to-resource mapping component of the framework as our results show that traditional mapping algorithms used with best effort jobs do not perform well for ARs. The paper proposes a set of algorithms for mapping ARs and investigates their performance in detail. The paper then presents a novel algorithm that outperforms a number of other algorithms in almost every respect for a wide range of workload parameters. Rigorous experimentation proves the efficacy of our algorithm and brings important insights into the dynamics of the system
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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