Flyover: a technique for achieving high performance in CORBA-based systems with limited heterogeneity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inter-operability in heterogeneous distributed systems is often provided with the help of CORBA compliant middleware. Many distributed object-computing systems, however, are characterised by limited heterogeneity. Such systems often contain a subset of components that are written in the same programming language and run on top of the same platform. Techniques that exploit such limited heterogeneity in systems for achieving high system performance are presented here. While components implemented using diverse programming languages and/or platform use a CORBA compliant middleware, the similar components can use a 'Flyover' that employs a separate path between the client and its server, and avoid a number of CORBA overheads. A prototype of a tool that is used for installing such flyovers in CORBA-based applications is implemented and is described. The performance of flyover-based systems is compared with those of pure CORBA-based systems that use commercial middleware products, under various workload and system parameters. A significantly large performance gain is achieved with the flyover for a range of workload parameters. Insights into system behaviour and performance developed from results of experiments with synthetic workload running on a network of PCs are presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it