Using existing instrumentation for transaction generation and performance analysis in distributed systems
Bibliographic record
Abstract
Modern information systems are composed of numerous applications working in concert to provide services to customers. Often, these applications are designed independently of one another, yet are fitted together in order to produce a meta-application. Each individual application within the meta-application generates information regarding its operational actions, yet no a priori design exists for the synthesis of information originating from these interdependent components. In this research, we are interested in correlating the information from these disparate sources in order to construct rules for defining dependencies within and between the data sources. These dependencies allow us to extract performance information about the meta-application and its components. To do this, we develop techniques for the automatic construction of transactions within the context of the meta-application. To this end, we present a methodology and a number of heuristic algorithms that allow for the definition of transactions with minimal input from the end user. From this transaction information, we utilize approaches that can generate performance feedback about each individual application as well as the aggregate performance of the meta-application as a whole. Ergo, we are interested in determining methodologies for extracting useful information from multiple data sources with no pre-meditated inter-connections in order to generate transactions and to provide information regarding the performance of the meta-applications and their components. Based upon the synthesis of these information sources and the automatic definition of transactions, we demonstrate an application of this approach through the generation of existing performance models such as stochastic models and analytic queuing models. Our works fills a much needed gap in previous research by using heuristics to suggest transaction definitions rather than leaving the transaction definition solely to the end user of the system. This has the practical business benefit of allowing the system administrator to work with applications and data sources outside of their administrative domain. Furthermore, this work minimizes the impact of organizational pluralism on the efforts of performance analysts who wish to understand the behaviour of meta-applications whose components may be deployed in several different administrative domains.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".