A Comparative Evaluation of EJB Implementation Methods
Bibliographic record
Abstract
As e-businesses are becoming ubiquitous, enhancing the performance and scalability of e-business systems has become an increasingly important topic of investigation. As Vitruvius (70-25 BC) put it succinctly 'function follows form', the ability of a system to perform well and scale easily is influenced by how the system itself is formed or implemented. A common approach to implement e-business systems is to make use of off-the-shelf enterprise middleware systems, such as a J2EE-compliant application server. Such middleware systems handle several, often complex, issues and thus simplify application development. They however allow developers the freedom not to use particular forms of support they offer and build their own mechanisms instead. This flexibility gives rise to many implementation methods. The work reported here evaluates these methods for Response Time and Throughput under various environments related to both client side (external to the system) and application execution (internal). To this end, one of the most widespread technologies used by the industry, the Enterprise Java Beans (EJB), is chosen; we have considered six commonly used implementation methods for an e-auction application and five different client-side and execution environments. The resulting study, which involves 78 experimental runs, identifies the strengths and the weaknesses of each implementation method under 13 different scenarios. It thus offers reliable guidelines for developers and valuable insights to researchers.
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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 | 0.045 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".