A Coarse-Grained Pessimistic Message Logging Scheme for Improving Rollback Recovery Efficiency
Bibliographic record
Abstract
As a common technology for fault tolerance and load balance, rollback-recovery faces the challenges of scalability and inherent variability in those long-running large-scale applications with grids as the computing infrastructure. Among the rollback recovery schemes, pessimistic message logging protocols (PMLPs) and coordinated checkpointing protocols (CCPs) are the most popular in practice. Although PMLPs are good in scalability, their fault-free overhead sometimes is prohibitive. CCPs introduce relatively lower overhead, but they are poor in scalability. This work employs partition strategy and introduces the concept of pessimism grain to rollback recovery, striking a balance between good scalability and acceptable overhead. For a partitioned system, a coarse-grained pessimistic message-logging protocol is proposed to achieve scalability and asynchrony both in fault-free execution and in fault recovery. The impact of pessimism grain on the performance is evaluated theoretically. Experimental results show that the pessimism grain is one of the key configuration parameters to reach a desired performance level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".