Application level performance optimizations for CORBA-based systems
Bibliographic record
Abstract
Middleware provides inter-operability in a heterogeneous distributed object computing environment. Common Object Request Broker (CORBA) is a standard for middleware proposed by OMG. Although inter-operability is achieved middleware often introduces overheads that impair system performance. This research is concerned with performance enhancement of CORBA-based systems by deploying appropriate techniques at the application level. The paper demonstrates that decisions made by the application software designer and programmer can have a large impact on the performance of a CORBA-based system. The paper presents a set of guidelines that can be used at the design and implementation levels for enhancing system performance. We focus on issues such as reduction of connection set up latency, appropriate techniques for parameter passing, impact of method placement on response time, performance implications of different ways of packing objects in servers and load balancing. Insights into system behavior that highlight the effectiveness of the guidelines as well as capture the relationship between the CORBA compliant middleware and overall application performance 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".