Self-Management of Operational Issues for 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
Science gateways, such as the Virtual Imaging Platform (VIP), enable transparent access to distributed computing and storage resources for scientific computations. However, their large scale and the number of middleware systems involved in these gateways lead to many errors and faults. This chapter addresses the autonomic management of workflow executions on science gateways in an online and non-clairvoyant environment, where the platform workload, task costs, and resource characteristics are unknown and not stationary. The chapter describes a general self-management process based on the MAPE-K loop (Monitoring, Analysis, Planning, Execution, and Knowledge) to cope with operational incidents of workflow executions. Then, this process is applied to handle late task executions, task granularities, and unfairness among workflow executions. Experimental results show how the approach achieves a fair quality of service by using control loops that constantly perform online monitoring, analysis, and execution of a set of curative actions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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