RPC automation: making legacy code relevant
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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