Adaptive Deadlock Detection and Resolution in Real-Time Distributed Environments
Bibliographic record
Abstract
In real-time distributed transaction processing, deadlocks must be detected and resolved. Timeouts are not a viable option because they lead to lost work and missed deadlines. We have proposed a suite of deadlock detection protocols and a resolution protocol which carry a varying degree of (generally low) overhead. The protocols behave differently under varying load conditions and transaction characteristics. Further, the invocation period of these protocols can be controlled to improve performance when the overhead tends to become large. The performance of these protocols has been demonstrated using a distributed real-time transaction processing simulator which provides an interactive interface to set parameters, and also has provisions to select from a variety of concurrency control, priority assignment, workload distribution and other protocols. The impact of transaction workload, underlying system configuration, resource availability, detection rates, and congestion on each of the proposed protocols is observed and presented. In general, the multi-cycle detection protocol demonstrated the most superior performance over a broad range of parameters. The results presented in this paper were obtained from over 147,000 simulations.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".