Intelligent Selection of Fault Tolerance Techniques on the Grid
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
The emergence of computational grids has lead to an increased reliance on task schedulers that can guarantee the completion of tasks that are executed on unreliable systems. There are three common techniques for providing task-level fault tolerance on a grid: retrying, replicating, and checkpointing. While these techniques are varyingly successful at providing resilience to faults, each of them presents a tradeoff between performance and resource cost. As such, tasks having unique urgency requirements would ideally be placed using one of the techniques; for example, urgent tasks are likely to prefer the replication technique, which guarantees timely completion, whereas low priority tasks should not incur any extra resource cost in the name of fault tolerance. This paper introduces a placement and selection strategy which, by computing the utility of each fault tolerance technique in relation to a given task, finds the set of allocation options which optimizes the global utility. Heuristics which take into account the value offered by a user, the estimated resource cost, and the estimated response time of an option are presented. Simulation results show that the resulting allocations have improved fault tolerance, runtime, profit, and allow users to prioritize their tasks.
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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 | 0.001 | 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