A Unified Scheduling Algorithm for Grid Applications
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
A grid scheduler maps efficiently the resources, available to a grid resource-service provider, to the applications submitted by the users. For efficient mapping, the scheduler should be able to use the information about the availability of computeresources and communication resources,communication delays, the application and the communication latencies and prior reservation of resources, if any. Moreover the characteristics of the heterogeneous set of compute-nodes and the characteristics of the communication network must also be considered by a grid scheduler. A generalized scheduler should be able to handle a diverse set of jobs, with arbitrary inter-dependencies among processes and arbitrary communication channel delays. The Unified Grid Scheduling Algorithm (UGSA), presented in this paper, is able to use all of the above information to respond to the changing workload and environmental conditions without causing much of an overhead. UGSA is the first scheduler, which is able to take care of both the communication latencies and application latencies while mapping DAG-model of applications. It has a genetic algorithm mode, which can be used, when jobs with requirements of high processing are being mapped. The scheduler has been tested extensively.
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