Towards an Efficient Online Causal-Event-Pattern-Matching Framework
Bibliographic record
Abstract
Event monitoring and logging, that is, recording the communication events between processes, is a critical component in many highly reliable distributed systems. The event logs enable the identification of certain safety-condition violations, such as race conditions and mutual-exclusion violations, as safety is generally contingent on processes communicating in a specific causally ordered pattern. Previous e orts at finding such patterns have often focused on online techniques, which are unable to identify operational problems as they occur. Online monitoring tools exist but they are often restricted to identifying a specific violation condition, such as a deadlock or a race condition, using dedicated data structures. We address the more general problem of detecting causally related event patterns that can be used to identify various undesired behaviours in the system. The main challenge for online pattern matching is the need to store the partial matches to the pattern, as they may combine with future events to form a complete match. Unlike pattern matching in most other domains, causally ordered patterns can span a potentially unbounded number of events and efficiently searching through this large collection poses a significant challenge. In this paper, we introduce OCEP, an efficient online causalevent- pattern-matching framework that bounds the number of partial matches it stores by reporting only a representative subset of pattern matches. We define a subset of matches as representative if it has at least one occurrence of each event in the pattern on each process, which is applicable for a large class of distributed applications. With this definition, OCEP introduces a backtracking algorithm to efficiently find a representative subset from the history of events. An evaluation of the framework shows that OCEP is capable of handling several frequently occurring violation patterns at the event rates of some representative distributed applications.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".