RPC automation: making legacy code relevant
Bibliographic record
Abstract
Due to the well-known issues with Remote Procedure Calls (RPC), the rather simple idea of modifying legacy applications - that have low spatial locality to the data they need to process - to execute all of their procedures via RPC is not a feasible option. A more realistic and feasible alternative is to provide a self-management mechanism that can dynamically monitor and alter the execution of an existing application by selectively modifying certain procedures to execute remotely when it is necessary to improve spatial locality. In this paper we describe the motivations behind such a self-management mechanism, and outline an initial design. In addition, we introduce our vision for the required profiling component of these applications. As such, we introduce the Automated Legacy system Remote Procedure Call mechanism (ALRPC). It automatically converts existing monolithic C applications into a distributed system semi-automatically. Thus automation is a key criterion for successfully competing with existing remote procedure tools for legacy applications and with newer solutions such as SOAP and REST [12], [21]. ALRPC is the core component to convert monolithic applications into distributable self-adaptive RPC systems. The empirical results collected from our initial experiments show that our mechanism's level of automation outperforms existing industry strength tools and improves development time. At the same time our mechanism is able to correctly function with a significant code base and shows acceptable performance in initial tests.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".