Flyover: a technique for achieving high performance in CORBA-based systems with limited heterogeneity
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".