Towards just-in-time middleware architectures
Bibliographic record
Abstract
Middleware becomes increasingly important in building distributed applications. Today, conventional middleware systems are designed, implemented, and packaged prior to their applications. We argue that with this middleware construction paradigm it is often difficult to meet the challenges imposed by application specific customization requirements. We propose to reverse this paradigm by automatically synthesizing middleware structures as the result of reasoning about the distribution needs of the user application of middleware. We term this type of post-postulated middleware Just-in-time middleware (JiM). In this paper, we present our initial design and present an evaluation of the JiM paradigm through Abacus, a CORBA middleware implementation based on the aspect oriented refactoring of an industrial strength object request broker. In addition, we present Arachne, the Abacus synthesizer, which integrates source analysis, feature inference, and implementation synthesis. Our evaluations show that, through automatic synthesis alone, Abacus is able to support diversified application domains with very flexible architectural compositions and versatile resource requirements as compared to conventional pre-postulated approaches.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".